Object Re-identification is a key technology for enabling mobile visual search, virtual reality and augmented reality, and a variety of security and surveillance applications. One key problem in re-identification is to have effective key point feature aggregation schemes that can preserve recall performance in short listing, while offering indexing and hashing efficiency. In the MPEG Compact Descriptor for Visual Search (CDVS) Standardization effort, the Scalable Compressed Fisher Vector (SCFV) was proved to be most efficient in this role. In this paper we develop a novel aggregation scheme that captures the subspaces where key points reside, and uses the Grassmannian distance metric to discriminate the aggregated information. Simulation demonstrates that the proposed scheme captures discriminative information complementary to the Fisher Vector (FV) aggregation, and can significantly improve the matching performance.

Key point features are very effective tools in image matching and key point feature aggregation is an effective scheme for creating a compact representation of the images for visual search. This solution not only achieves compression, but also offers the benefits of better accuracy in matching and indexing efficiency. Research is active in this area and recent results on Fisher Vector based aggregation have shown to be very effective in a number of application scenarios. In this paper, we present a new direct aggregation scheme that is adaptive to the descriptor distributions from individual images and does not enforce a single generative model such as GMM in the Fisher Vector type aggregation. Moreover, it achieves better compression as well as image matching accuracy. Simulation results with the image identification data set from MPEG Compact Descriptor for Visual Search (CDVS) effort demonstrate the effectiveness of this approach.

Automatic face recognition is prevalent in a wide range of systems these days and it is critical to explore new techniques in order to enhance the state of the art. In this paper, we analyze the Region Covariance Matrix (RCM) and its enhancement based on Sigma sets as a feature extraction procedure for face images. The RCM features encode the covariance of various low level features, e.g., pixel intensities and gradients. Sigma sets, on the other hand, reduce the computational complexity of comparing two RCMs. Based on our experiments on the Labeled Faces in the Wild (LFW) dataset, we show that the proposed technique outperforms the popular Local Binary Patterns (LBP) technique and is on par with other better performing techniques that use complex classifiers.

The objective in developing compact descriptors for visual image search is building an image retrieval system that works efficiently and effectively under bandwidth and memory constraints. Selecting local descriptors to be processed, and sending them to the server for matching is an integral part of such a system. One such image search and retrieval system is the Compact Descriptors for Visual Search (CDVS) standardization test model being developed by MPEG which has an efficient local descriptor selection criteria. However, all the existing selection parameters in CDVS are based on low-level features. In this paper, we propose two “mid-level” local descriptor selection criteria: Visual Meaning Score (VMS), and Visual Vocabulary Score (VVS) which can be seamlessly integrated into the existing CDVS framework. A mid-level criteria explicitly allows selection of local descriptors closer to a given set of images. Both VMS and VVS are based on visual words (patches) of images, and provide significant gains over the current CDVS standard in terms of matching accuracy, and have very low implementation cost.

Image matching and search is gaining significant commercial importance nowadays due to various applications it enables such as augmented reality, image-queries for internet search, etc. Many researchers have effectively used color information in an image to improve its matching accuracy. These techniques, however, cannot be directly used for large scale mobile visual search applications that pose strict constraints on the size of the extracted features, computational resources and the system accuracy. To overcome this limitation, we propose a new and effective technique to incorporate color information that can use the SIFT extraction technique. We conduct our experiments on a large dataset containing around 33, 000 images that is currently being investigated in the MPEG-Compact Descriptors for Visual Search Standard and show substantial improvement compared to baseline.

Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have been made to statistically model the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either make unrealistic assumptions about the model or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the nonmatch probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and exemplar prints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent prints using two exemplar print databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also discuss a practical scenario where a latent examiner uses the proposed framework to compute the evidential value of a latent-exemplar print pair comparison.

Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.

Fingerprint evidence is routinely used by forensics and law enforcement agencies worldwide to apprehend and convict criminals, a practice in use for over 100 years. The use of fingerprints has been accepted as an infallible proof of identity based on two premises: (i) permanence or persistence, and (ii) uniqueness or individuality. However, in the absence of any theoretical results that establish the unique ness or individuality of fingerprints, the use of fingerprints in various court proceedings is being questioned. This has raised awareness in the forensics community about the need to quantify the evidential value of fingerprint matching. A few studies that have studied this problem estimate this evidential value in one of two ways: (i) feature modeling, where a statistical (generative) model for fingerprint features, primarily minutiae, is developed which is then used to estimate the matching error and (ii) match score modeling, where a set of match scores obtained over a database is used to estimate the matching error rates. Our focus here is on match score modeling and we develop metrics to evaluate the effectiveness and reliability of the proposed evidential measure. Compared to previous approaches, the proposed measure allows explicit utilization of prior odds. Further, we also incorporate fingerprint image quality to improve the reliability of the estimated evidential value.

This paper describes our recent analysis on the security and privacy of biometric feature vectors obtained from fingerprint minutiae. A large number of contiguous regions (cuboids) are selected at random in the minutiae space, and several new features are extracted from the minutiae inside each such cuboid. Specifically, the features are extracted from the average minutia coordinate within a cuboid, the standard deviation of the minutiae coordinates, and the aggregate wall distance, i.e., the sum of distance of each minutia from the boundary of the cuboids. In terms of matching performance on a public database, the feature vectors provide an equal error rate of 3 % even if the imposter is allowed to use the same local patches as the genuine user. Performance within a secure biometrics framework is evaluated by applying an LDPC code to the feature vectors and storing only the syndrome at the access control device, for use in authentication. The paper concludes with a discussion on methods to analyze security and privacy of biometric systems that use such local-aggregate-based feature vectors in a secure biometric recognition framework. This discussion highlights security attacks via template injection, spoofing, and cancelability compromises and also considers the difficulty of privacy attacks via template inversion.

Fuzzy vault has been shown to be an effective technique for securing fingerprint minutiae templates. Its security depends on the difficulty in identifying the set of genuine minutiae points among a mixture of genuine and chaff points and reconstructing the secure polynomial using the evaluations (ordinate values) available for each point in the vault. We show that the security of fuzzy vault can be improved by ldquoencryptingrdquo these polynomial evaluations using a fuzzy commitment scheme. This encryption makes it difficult for an adversary to decode the vault even if the correct set of minutiae is selected. We use minutiae descriptors, which capture orientation and ridge frequency information in a minutiapsilas neighborhood, for securing the polynomial evaluations. This modification leads to a significant increase in both the security (number of tries an adversary has to make in order to guess the secure key) and matching accuracy of the vault. We validate our results on FVC2002 DB2 and show that false accept rate (FAR) is reduced from 0.7% to 0.01% at a genuine accept rate (GAR) of 95%. At the same time, vault security as measured in terms of min-entropy, is increased from 31 bits to 47 bits in case a perfect code is used.