216 shades of gray: high bit-depth projection using light intensity control.

Projectors create grayscale images by outputting a series of bitplanes or binary images within a short time period. While this technique works well for projecting low bit-depth (8-bit) images, it becomes infeasible for high bit-depth (say, 16-bit) projection - a capability that is increasingly desirable in many applications including cinemas and gaming. Existing designs for high bit-depth projection rely on multiple spatial light modulators and, as a consequence, their costs and complexities are usually far beyond the average consumer. In this paper, we describe a technique for high bit-depth projection using a single light modulator by adopting intensity-modulated light sources. With the proposed light intensity modulation, we show that the number of bitplanes required to achieve a desired bit-depth can be dramatically reduced - by marginally trading-off the brightness of the projected image. Hence, given a spatial light modulator of a fixed bandwidth for projecting bitplanes, the proposed projector design can achieve higher bit-depth as well as expanded color gamut while achieving the same video framerate as conventional projectors. The proposed design involves a minor modification to traditional projector designs, namely intensity modulation of the light sources, and hence, can be adopted widely by both traditional low bit-depth projectors and modern high dynamic-range projectors. Finally, we present a hardware prototype to showcase and validate the performance of the proposed design.

Noninvasive diabetes mellitus detection using facial block color with a sparse representation classifier.

Diabetes mellitus (DM) is gradually becoming an epidemic, affecting almost every single country. This has placed a tremendous amount of burden on governments and healthcare officials. In this paper, we propose a new noninvasive method to detect DM based on facial block color features with a sparse representation classifier (SRC). A noninvasive capture device with image correction is initially used to capture a facial image consisting of four facial blocks strategically placed around the face. Six centroids from a facial color gamut are applied to calculate the facial color features of each block. This means that a given facial block can be represented by its facial color features. For SRC, two subdictionaries, a Healthy facial color features subdictionary and DM facial color features subdictionary, are employed in the SRC process. Experimental results are shown for a dataset consisting of 142 Healthy and 284 DM samples. Using a combination of the facial blocks, the SRC can distinguish Healthy and DM classes with an average accuracy of 97.54%.

A bayesian approach to deformed pattern matching of iris images.

We describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: It normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications.

Wavelet packet correlation methods in biometrics.

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices.

Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [Appl. Opt. 26, 3633 (1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University's Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.

Biometric verification with correlation filters.

Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification. In particular, advanced correlation filters, such as synthetic discriminant function filters, can offer very good matching performance in the presence of variability in these biometric images (e.g., facial expressions, illumination changes, etc.). We investigate the performance of advanced correlation filters for face, fingerprint, and iris biometric verification.

Polynomial distance classifier correlation filter for pattern recognition.

We introduce what is to our knowledge a new nonlinear shift-invariant classifier called the polynomial distance classifier correlation filter (PDCCF). The underlying theory extends the original linear distance classifier correlation filter [Appl. Opt. 35, 3127 (1996)] to include nonlinear functions of the input pattern. This new filter provides a framework (for combining different classification filters) that takes advantage of the individual filter strengths. In this new filter design, all filters are optimized jointly. We demonstrate the advantage of the new PDCCF method using simulated and real multi-class synthetic aperture radar images.

Two class minimax distance transform correlation filter.

A new correlation filter formulation (that we refer to as the minimax distance transform correlation filter (MDTCF) is presented that minimizes the average squared distance from the filtered desired (or true-) class training images to a filtered reference image while maximizing the mean squared distance of the filtered undesired (or false-) class training images to this filtered reference image. This approach increases the separation between the false-class correlation outputs and the true-class correlation outputs. Classification can be performned using the squared distance of a filtered test image to the chosen filtered reference image. We show that the previously introduced distance classifier correlation filter (DCCF) is similar to a special case of MDTCF. We also examine the differences between the DCCF and the MDTCF, and show that MDTCF can offer increased discrimination performance. Also, MDTCF performance is evaluated on two different face databases.