Field of the invention

[0001] The present invention concerns method of detecting a falsified presentation to a vascular recognition system and in particular to such a method in which images of an entity which is presented to the system are obtained when the entity is illuminated at different wavelengths, and using those image to determine the probability that the entity is falsified.

[0002] The present invention also concerns the use of hand palm and finger as biometric characteristics.

Description of related art [0003] The recognition of persons by means of their vascular network pattern (i.e. the pattern of the person's veins and arteries, typically 3mm below the surface of the skin) is becoming more wide spread. This practice of recognition of an individual through their vascular network pattern is commonly known as vascular recognition. Typically the network pattern of the persons hand palm or finger is used in the practice of vascular recognition.

[0004] The vascular image of veins is typically captured under near- infrared (NIR) illumination using a reflection method or a transmission method. In the reflection method, the reflected NIR light that is emitted from the object is captured using a CCD camera with an infrared filter. In the transmission method, the NIR light is captured after transmission through the object (finger for example). The principle of each method is that veins that carry deoxygenated hemoglobin absorb light within the wavelength from 760nm to 880nm (NIR range). Hence, under NIR illumination veins appear as dark lines that can be extracted and used for user identification or authentication. [0005] Vascular recognition is becoming more popular as it has very low error rates due to the distinct differences which exist between individual's vascular network patterns. A further advantage is that the method of recognition leaves no trace (unlike fingerprints) hence making it more difficult to make a copy to forge a fake.

[0006] However, current vascular recognition systems and methods are not without limitations. For example it is possible for an unrecognized person to fool existing systems by obtaining an image of the vascular network pattern of a recognised person and to present a fake, such as a print-out on paper, to the system. Presenting a falsified biometric on the sensor of a biometric recognition system is known as "spoofing" or presentation attack.

[0007] A common way to build a fake for vascular recognition is to print solely the vein image on a uniformly white paper. Since the toner from most laser printer absorbs the near infrared wavelength, a typical vascular recognition system based on images captured in NIR field could easily be spoofed by this attack.

[0008] Patent application US201 1304720 describes a device and a method of automated personal identification that uses finger vein and finger surface images acquired at the same time to extract near-infrared (NIR) and visible features. These features are computed to provide matching scores and are combined to determine whether the person is genuine or an imposter.

[0009] Patent application US2009232362 describes a biometric

authentication based on simultaneous fingerprint and vein images acquisition in which a sequence of vein images and a sequence of fingerprints images are taken at short interval to construct one vein image, and one finger print image in order to improve the authentication quality.

[0010] Each of the solutions of the prior art exclusively use either still images, or a series of images, to detect falsification or spoofing. Such solutions are vulnerable for spoofing as a falsification need only be realistic under a single condition. In addition the solutions use a single means only for detecting detect falsification or spoofing which makes these solutions more vulnerable to spoofing. [0011] It is an aim of the present invention to mitigate or obviate at least some of the above-mentioned disadvantages.

[0012] In particular it is an aim of the present invention to provide a method which can detect a falsified presentation to a vascular recognition system more reliably. Brief summary of the invention

[0013] According to the present invention there is provided an anti- spoofing method for a biometric vascular recognition system, the method comprising the steps of:

illuminating an entity which is presented to the vascular recognition system, with light which has wavelengths in a first wavelength range only;

capturing a sequence of images of the entity over a time period, when the entity is illuminated with light which has wavelengths in a first wavelength range;

computing a plurality of first binary descriptors representative of the spatial texture of at least one of said images in the sequence of images, and of the manner in which some elements of the image change between successive images in the sequence, over a plurality of images;

illuminating the entity which is presented to the vascular recognition system, with light which has wavelengths in a second

wavelength range only,

capturing an image of the entity, when the entity is illuminated with light which has wavelengths in a second wavelength range, to provide at least one static image;

computing a second binary descriptor for each of said at least one static images; determining if the entity which is presented to the vascular recognition system is a spoof, based on a plurality of first and second binary descriptors.

[0014] The biometric vascular recognition system may use an image of the vascular network in the hand palm, in the wrist, in the dorsal hand, in a finger and/or in the retina for example.

[0015] Advantageously, the method of the present invention uses both the texture of an image of the entity and the manner in which some elements (such as pixel or features) change between successive images of the entity in the sequence, in order to detect falsification; thus providing for a more reliable detection of a spoof.

[0016] It will be understood that the entity could be simultaneously illuminated with light which has wavelengths in a first wavelength range only and with light which has wavelengths in a second wavelength range only. Alternatively the entity could be consecutively illuminated with light which has wavelengths in a first wavelength range only and then

illuminated with light which has wavelengths in a second wavelength range only, so that the entity is exclusively illuminated with light which has wavelengths in a first wavelength range only for a first time period, and is exclusively illuminated with light which has wavelengths in a second wavelength range only for a second time period.

[0017] Advantageously, this method of the present invention uses a combination of both multi-spectrum (i.e. use of images captured with illumination in first and second wavelength ranges) and a multi-algorithm (features of static images and dynamic texture of the sequence of images) to detect falsification, thus providing for a more reliable detection of a spoof.

[0018] The step of determining if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of first dynamic binary descriptors and second binary descriptors may comprise:

determining that the entity which is presented to the vascular recognition system is a spoof if the plurality of first dynamic binary descriptors represent that there is no change in the texture of said some elements between successive images in the sequence,

or, determining if the plurality of first dynamic binary descriptors correspond with a plurality of predefined expected dynamic binary descriptors, and determining that the entity which is presented to the vascular recognition system is a spoof if the plurality of first dynamic binary descriptors fails to correspond with a plurality of predefined expected dynamic binary descriptors.

[0019] The step of determining if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of first dynamic descriptors, may comprise,

using a classifier trained with a plurality of reference dynamic binary descriptors to determine whether the dynamic changes of texture correspond to a real biometric characteristic or to a spoof.

[0020] The step of determining if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of binary descriptors, may comprise,

using a discriminative method to minimize a loss function to determine the degree of similarity between the plurality of binary descriptors and a plurality of reference dynamic binary descriptors; and/or using a generative technique which maximizes a likelihood function to determine the degree of similarity between the plurality of binary descriptors and said plurality of reference dynamic binary

descriptors.

[0021] Preferably the first and second wavelength ranges are non- overlapping ranges. In one preferred embodiment, the first wavelength range may be in the near infrared (NIR) spectrum and used for revealing the vein pattern as well as changes of the vein pattern between frames. The second wavelength may be in the visible spectrum and reveal the skin structure.

[0022] This multi-spectrum method has the advantage that it becomes more difficult to build a fake that is realistic both in the visible spectrum and in the NIR spectrum. For example, a print-out of a vein pattern on a white paper will miss the texture of the skin in the visible spectrum range.

[0023] According to an embodiment of the present invention there is provided an anti-spoofing method for a biometric vascular recognition system, the method comprising the steps of:

for each of a plurality of different wavelength ranges, performing the following steps (a)-(g), to provide a plurality of first and second binary descriptors,

(a) illuminating an entity which is presented to the vascular recognition system, with light which has wavelengths in a first wavelength range;

(b) capturing a sequence of images of the entity over a time period, when the entity is illuminated with light which has wavelengths in said first wavelength range;

(c) computing a plurality of first binary descriptors representative of the texture of at least one of said images in the sequence of images, and of the manner in which some elements of image change between successive images in the sequence, over at least some images of said sequence;

(e) illuminating the entity which is presented to the vascular recognition system, with light which has wavelengths in a second wavelength range,

(f) capturing an image of the entity, when the entity is illuminated with light which has wavelengths in a second wavelength range, to provide at least one static image;

(g) computing a second binary descriptor for each of said at least one static images;

determining if the entity which is presented to the vascular recognition system is a spoof, based on said plurality of first binary descriptors, and said plurality of second binary descriptors.

[0024] The plurality of different wavelength ranges may comprise the ranges 350nm-740nm (visible), 740nm-1400nm (NIR), 760nm-880nm (sub- NIR) and 8000nm-1 5000nm (LWIR).

[0025] According to a further aspect of the present invention there is provided a method of biometric vascular recognition comprising the steps of,

(a) performing any of the above-mentioned methods, to determine if an entity presented to the vascular recognition system by a user is a spoof;

(b) identifying the user, wherein the identification may bebased for example on at least said first and second binary descriptors;

(c) recognising the user if step (a) indicates that the entity which is presented is a real biometric characteristic (for example a real hand palm) and if user has been identified in step (b).

[0026] Preferably steps (a) and (b) are performed in parallel.

[0027] In one example, the step of identifying the user may comprise, comparing said first and second binary descriptors with one or more reference binary descriptors each representative of texture of a reference image. Other identification methods may be used.

[0028] According to a further aspect of the present invention there is provided a biometric vascular recognition system, comprising:

a light source configured to respectively illuminate an entity which is presented to the system, with light which has exclusively first and second wavelength ranges only;

image capturing means which can capture an image and a sequence of images of the entity when the entity is illuminated by the light source to provide a sequence of images and at least one static image;

a processor which is configured to compute a plurality of first binary descriptors representative of the texture of at least one of said images in the sequence of images and of the manner in which some elements of the image change between successive images in a sequence of images of the sequence, and to compute second binary descriptors for each of said at least one static images, and to determine if the entity which is presented to the vascular recognition system is a spoof, based on the plurality of first and second binary descriptors.

[0029] The present invention is also related to a computer readable storage medium having recorded thereon a computer program, the computer program comprising an algorithm capable of any of the methods which are herein described.

Brief Description of the Drawings

[0030] The invention will be better understood with the aid of the description of an embodiment given by way of example and illustrated by the figures, in which:

Figure 1 is a flow chart illustrating the steps involved in an anti- spoofing method for a biometric vascular recognition system, according to an embodiment of the present invention;

Figure 2 is a block diagram illustrating a method of vascular recognition according to a further aspect of the present invention;

Figure 3 shows a block diagram illustrating the features of a biometric vascular recognition system;

Figure 4 illustrates the computation of a binary descriptor based on a Local Binary Pattern (LBP).

Figure 5 illustrates a vascular anti-spoofing system based on a multi-algorithm and multi-spectral framework. Detailed Description of possible embodiments of the Invention

[0031] Figure 1 is a flow chart illustrating the steps involved in an anti- spoofing method for a biometric vascular recognition system, according to an embodiment of the present invention. The vascular recognition system may use for example vein patterns in the hand palm, in the fingers, etc.

[0032] The method comprises the steps of:

illuminating an entity which is presented to the vascular recognition system with light which has wavelengths in a first wavelength range only (1 );

capturing a sequence of images of the entity over a time period, when the entity is illuminated with light which has wavelengths in a first wavelength range only (2);

computing a plurality of first binary descriptors representative of the texture of at least one of said images in the sequence of images (3), and of the manner in which some elements of image change between successive images in the sequence, (4);

illuminating the entity which is presented to the vascular recognition system, with light which has wavelengths in a second

wavelength range only (5),

capturing an image of the entity, when the entity is illuminated with light which has wavelengths in a second wavelength range only, to provide at least one static image (6);

computing second binary descriptors for each of said at least one static images (7);

determining if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of first dynamic binary descriptors and said first binary descriptor (8).

[0033] Advantageously, the method of the present invention uses both the texture of an image of the entity and the manner in which some elements of texture change between successive images in the sequence, in order to detect falsification; thus providing for a more reliable detection of a spoof.

[0034] For example, if a user attempted to spoof the recognition system by presenting a fake of a biometric characteristic (for example a print copy of a hand palm) to the recognition system, the system will capture an image of the fake and will identify if the texture of that image corresponds to expected textures (the expected textures will be textures of images of real biometric characteristics). In the present example since the image will be an image of a fake, and not an image of a real biometric characteristic, the texture of the image will not correspond to an expected texture, so it can be determined that the presentation is a spoof.

[0035] In addition to this the method of the present invention takes a second measure to detect spoofing: since the fake produces a still image of a biometric vascular system there would be no difference in texture of successive images in the sequence (or only changes due to a move of the fake between frames); this is in contrast to if a real hand-palm has been presented to the recognition system; because of the flow of blood through the vascular system in the biometric characteristic (for example in the hand palm), the successive images in the sequence of images will differ, thus providing for a changes in texture of successive images in the sequence.

[0036] In the present invention, the first dynamic binary descriptors are representative of the manner in which some elements of texture change between successive images in the sequence, over several frames and/or over the whole sequence; thus a spoof will be detected if the plurality of first dynamic binary descriptors indicate that there is no difference in texture between successive images in the sequence, or if the changes do not correspond to some expected patterns of change.

[0037] In the present example, because the fake is a still image of a hand-palm vascular system the first dynamic binary descriptors will indicate that there is no difference in texture between successive images in the sequence (or only differences corresponding to a move of the whole fake), thus spoofing will be detected.

[0038] The user may move the fake when presenting it to the

recognition system; this will create a difference in texture between successive images in the sequence, so that the first dynamic binary descriptors will indicate that there are some differences in texture between successive images in the sequence. Accordingly, the method may involve determining if the plurality of first dynamic binary descriptors correspond with a plurality of predefined expected dynamic binary descriptors, and determining that the entity which is presented to the vascular recognition system is a spoof if the plurality of first dynamic binary descriptors fails to correspond with a plurality of predefined expected dynamic binary descriptors. Thus in the case where the user moves the fake when

presenting it to the recognition system, although the first dynamic binary descriptors will indicate that there is some differences in texture between successive images in the sequence, the first dynamic binary descriptors will fail to correspond to any predefined expected dynamic binary descriptors so that the fake presented will still be recognised as a spoof.

[0039] In the most preferable embodiment, since the first dynamic binary descriptors have been computed from images which were captured with light which has wavelengths in a first range only (for example light in the near infrared field), and the second binary descriptors have been computed from images which were captured with light which had wavelengths in a second range only (for example visible light), the method uses a combination of both multi-spectrum (first and second wavelength ranges) and a multi-algorithm (features of static images and dynamic texture of the sequence of images) to detect a spoof. This provides for a more reliable detection of a spoof.

[0040] For example, the first and second wavelength ranges may be non-overlapping ranges; for example the first wavelength range may be 741 nm-1400nm (near-infra red spectrum) and the second wavelength range may be 350nm-740nm (visible spectrum). It is nearly impossible to provide a spoof which will appear realistic to the recognition system under the two different light conditions.

[0041] If a print-out of a vascular system of an authorised user was presented to the recognition system, the print-out may appear to be realistic under the near infrared light in the range 741 nm-1400nm since the ink on the print-out will absorb the light with wavelength in this range. However, this print-out is likely to miss the skin texture that will be seen when a real biometric characteristic is illuminated with light in the visible range, i.e. light with wavelengths in the range 350nm-740nm as all the features of the print out will reflect the light with wavelengths in the range 350nm-740nm. Thus the print-out will be revealed as a spoof when illuminated with light which has wavelengths in the visible range only.

[0042] It will be understood that the entity could be simultaneously illuminated with light which has wavelengths in a first wavelength range only and light which has wavelengths in a second wavelength range only. For example the entity could be illuminated simultaneously with light which has wavelengths in the range 350nm-740nm (visible spectrum), and with light which has wavelengths in the range 741 nm-1400nm (near-infra red spectrum). Alternatively the entity could be consecutively illuminated with light which has wavelengths in a first wavelength range only and then illuminated with light which has wavelengths in a second wavelength range only. For example, the entity could be illuminated consecutively with light which has wavelengths in the range 350nm-740nm (visible spectrum), and with light which has wavelengths in the range 741 nm-1400nm (near- infra red spectrum), so that the entity is exclusively illuminated with light which has wavelengths in a 350nm-740nm (visible spectrum) only for a first time period, and is then exclusively illuminated with light which has wavelengths in the range 741 nm-1400nm (near-infra red spectrum) only for a second time period. [0043] In the method of the present invention the computation of the second binary descriptors which represent the spatial texture of an image may be done using a texture descriptor. In one example, a Local Binary Pattern (LBP) is used as texture descriptor. A LBP descriptor converts a pixel into a decimal code taking into account the neighbourhood of this pixel in the image plane. In the example of Figure 4, the computation of a Local Binary Pattern involves a binary comparison of each pixel with the surrounding ones; a 0 value is determined for all pixels having a brightness value lower than the brightness value at the center, and a " 1 " when the brightness is higher. The resulting binary value (001 1 1001 in the example) corresponds to a decimal value (57) which depends on the direction and sense of the brightness change at each pixel. A histogram may then be formed from a plurality of values over the whole image or over several point of interests.

[0044] Therefore the second binary descriptor may be represented with a series of decimal values, each value depending on a pixel value and the the values of the surrounding pixels in the spatial plane. [0045] The pixel value may be the pixel brightness and/or colour/ and/or saturation, etc, or any combination.

[0046] The element of image might be pixels, or groups of pixels, or other features. The change may reflect change in brightness, and/or change of intensity, colour, hue, etc, of the pixels. [0047] The first dynamic binary descriptors may be represented with binary numbers representative of spatial and temporal changes of elements of image. For example, all pixel values (in space and time) over a series of frames are transformed into binary descriptors representing variations of those values with values of (spatially) surrounding pixels and of previous and next corresponding pixels. In one example a spatio-temporal texture descriptor named LBP-TOP is used for this purpose. The LBP-TOP operator converts each pixel of a series of frames into a value, by considering the sequence of images as a three dimensional volume XYT and operating the above-described LBP on each plane ΧΥ,ΧΤ and YT. Thus, each element of the image, for example each pixel, might be represented by a value representing the difference between this pixel and the spatially surrounding pixels, as well as the temporal differences between this pixel and the previous and next corresponding pixels.

[0048] In one embodiment, the first binary descriptors are

representative of image element changes over a given time interval, typically between 3 and 10 frames.

[0049] In one example, the step of determining if the entity which is presented to the vascular recognition system is a real biometric

characteristic or a spoof may involve a comparison between a plurality of first dynamic binary descriptors with one or more pluralities of reference dynamic binary descriptors. In addition said second binary descriptors may be compared with one or more reference binary descriptors. The reference dynamic binary descriptors are representative of the manner in which some elements of texture change between successive images in one or more sequences of reference images, and, the one or more reference binary descriptors with which said second binary descriptors are compared, are each representative of texture of a reference image. It will be understood that the reference dynamic binary descriptors and the reference binary descriptors may be formed from images of valid biometric characteristics retrieved by the recognition system. [0050] The reference dynamic binary descriptors to which the first dynamic binary descriptors are compared and reference binary descriptors with which said second binary descriptors are compared, provide a measure of the authenticity of the entity presented to the recognition system. If the first dynamic binary descriptors are too dissimilar to all of the reference dynamic binary descriptors then it can be determined that the entity presented to the recognition system is a spoof. Likewise, if the second binary descriptors are too dissimilar to all of the reference binary

descriptors it can be determined that the entity presented to the

recognition system is a spoof. Accordingly, in the present invention there may be a dissimilarity threshold set for each of the dynamic binary descriptors and binary descriptors; exceeding this dissimilarity threshold will indicate that the entity presented is a spoof. [0051] In one preferred embodiment, the step of determining if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of first dynamic binary descriptors and said second binary binary descriptors, may comprise using at least one classifier to differentiate between real access and spoof attempts.

[0052] As such, classifiers are known in the art. The classifier may have been previously built or trained with data (both real-access and spoof attempts) and with an appropriately chosen machine learning algorithm such as, typically, a discriminative method or a generative method.

[0053] The classifier typically minimizes a loss function to determine the degree of similarity between the plurality of first dynamic binary

descriptors and a plurality of reference dynamic binary descriptors. Likewise a second classifier will use a discriminative method to minimize a loss function to determine the degree of similarity between the second binary descriptors and the reference binary descriptors. Alternatively, or

additionally, the classifier can use a generative technique which maximizes a likelihood function (e.g. Expectation-Maximization of a statistical model) to determine the degree of similarity between the plurality of first dynamic binary descriptors and said plurality of reference dynamic binary

descriptors. Likewise the classifier can use a generative technique which maximizes a likelihood function to determine the degree of similarity between the second binary descriptors and the reference binary

descriptors. [0054] The output of this step is composed of a single highly- discriminative score that maps directly to the probability of an attack, given the perceived data (a priori information).

[0055] A first classifier might be used for computing a first score based on the first binary descriptors. A second classifier might be used for computing a second score based on the second binary descriptors. The output of the first and second and classifiers may be combined in order to compute a consolidated score and to differentiate between real-access and spoof attempts.

[0056] In a preferred embodiment, the spatial and temporal texture changes in images captured at a first wavelength are represented by a single combined binary descriptor. The first classifier may then be used to compute a score and to differentiate between real-access and spoof attempts, based on this combined binary descriptor.

[0057] The score output of this first classifier may be combined with the output of a second classifier used to differentiate between real-access and spoof attempts, based on the second binary descriptor. The discrimination then depends both on the output of the first classifier and on the output of the second classifier.

[0058] In another embodiment, a first binary descriptor is used for representing variations of images over time at the first wavelength, and a distinct, third binary descriptor is used for representing spatial variations of images at the first wavelength. Two distinct classifiers might then be used for classifying the first and third binary descriptors.

[0059] It will be understood that two different algorithms, such as a frame-based algorithm based on descriptors of spatial changes (LBP) and a video-based spatio-temporal algorithm based on spatial and temporal descriptors (such as LBP-TOP), may also be used with light in the same wavelength range. In the example of Figure 5, descriptor-extraction modules ei to e ₈ based on both types of algorithms are used in four different wavelength ranges w, (visible, near-infrared, sub-near infrared, and thermal spectrum LWIR); each set of descriptors generated by each algorithm in each wavelength is then input to a classifier ci to Cs . The decision taken by each classifier (real or spoof; possibly associated with a probability) are then combined by a multi-spectral fusion system F.

Alternatively, several classifiers may be merged into a single classifier system exploiting descriptors output by different modules among the modules ei to e ₈ . [0060] According to a further aspect of the present invention there is provided a method of vascular recognition. Figure 2 is a block diagram illustrating an exemplary method of vascular recognition according to the present invention. The method of anti-spoofing in vascular recognition comprises the step of performing the multi-spectrum method illustrated in Figure 1, to determine if an entity presented to the vascular recognition system by a user is a real biometric characteristic or a spoof (30). In parallel to this step, a step of identifying the user is performed, based on NIR image only. Finally the step of recognising that the entity presented to the recognition system is valid, if it has been determined that the entity which is presented in a real biometric characteristic and if user has been identified (32).

[0061] It will be understood that the step of identifying the user may be carried out using any suitable means. For example the second binary descriptor may be compared with one or more reference binary descriptors each representative of texture of a reference image to determine the degree of similarity between the second binary descriptors and the reference binary descriptors. Other user identification methods may be used, including methods which don't rely on the second binary descriptors. [0062] Figure 3 is a diagram illustrating the features of a vascular recognition system 40 according to the present invention. The vascular recognition system 40 comprises, a light source 41 configured to

respectively illuminate an entity which is presented to the system, with light which has exclusively first and second wavelength ranges only; an image capturing means 42 which can capture an image and a sequence of images of the entity when the entity is illuminated by the light source to provide a sequence of images and at least one static image; and a processor 43.

[0063] The processor 43 is configured to compute a plurality of first binary descriptors representing the texture of at least one of said images in the sequence of images, and simultaneously representative of the manner in which some elements of image change between successive images in a sequence of images over the whole sequence, and to compute second binary descriptors for each of said at least one static images, and to determine if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of first dynamic binary descriptors and said second binary descriptors. To determine if the entity which is presented to the vascular recognition system is a real biometric characteristic or a spoof, based on the plurality of first dynamic binary descriptors and second binary descriptors, the processor is configured to perform any of the steps mentioned in the above-described methods.

[0064] It will be understood that there are two options for comparison depending on the application of the present invention: The invention may be used in the authentication of a person, in which case the biometric characteristic which is presented to the recognition system must match a single reference hand-palm which the recognition system has stored; or the invention may be used in an identification of a person, in which case the biometric characteristic presented to the recognition system must match one of a plurality of reference biometric characteristics which the

recognition has stored. [0065] According to an aspect of the invention, the first descriptors depending on temporal changes of patterns in the near infrared range, or in any range, is used to determine the heartbeat of the person.

[0066] It is to be recognized that depending on the embodiment, certain acts or events of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out all together (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain embodiments, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. For example, illuminations and image capturing at wavelengths in different ranges may be performed simultaneously or successively. [0067] Various modifications and variations to the described

embodiments of the invention will be apparent to those skilled in the art without departing from the scope of the invention as defined in the appended claims. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiment.