Iris is considered as one of the best biometric trait for human authentication due to its accuracy and permanence. However easy iris spoofing raise the risk of false acceptance or false rejection. Recent iris recognition research has made an attempt to quantify the performance degradation due to the use of contact lens. This study proposes a strategy to detect soft contact lens in visual pictures of the eye obtained using NIR sensor. The lens border is detected by considering small annular ring-like area near the outer iris boundary and locating candidate points while traversing along the lens perimeter. The system performance is evaluated over public databases such as IIITD-Cogent, UND 2010, IIITD-Vista along with our self created IITK database. The rigorous experimentation revels the superior performance of the proposed system as compared with other existing techniques.

This paper proposes an efficient method to detect any fraud document. It considers texture features, such as Local binary pattern and Gabor filters and performs a histogram matching to analyze the document. Texture features and RGB color information of each word in the document are extracted. Normalized histograms of two different images of a document are compared to generate a matching score for decision making. The advantage of the proposed method is that it is fully unsupervised and hence, it does not require any prior knowledge and is less time consuming. The method is found to be very efficient.

Large scale adaptation of a fingerprint based recognition system results in expansion of its database which increases the cost of identification and degrades the system performance. The paper proposes an efficient indexing technique that can scatter the effect of database escalation and maintains the system performance. It has proposed a fixed length feature vector built from each minutia, known as Coaxial Gaussian Track Code (CGTC). The proposed technique inserts feature vector into a Quantized Lookup Table (QLT) only once. As a result, it reduces both computational and memory costs. Since minutiae of all fingerprint images in the database are found to be well distributed in the quantized lookup table, it does not need rehashing. Experiments have been conducted over three fingerprint databases viz. FVC2002, FVC2004 and IITK-Sel500FP containing fingerprints from 100, 100 and 500 subjects respectively. Results have proven the superiority of the proposed indexing technique against well known geometric based indexing techniques.

Accurate fingerprint orientation is a prerequisite in fingerprint based recognition system. This paper proposes an algorithm for modeling the fingerprint orientation field by using a model based algorithm based on the weighted Legendre basis. Weights required in the modeling are obtained by using symmetric filters, such that: i) high weights should be assigned to the areas near singular points, ii) areas having uniform ridge-valley flow should be given high weights, and iii) areas containing bad quality due to dry/ wet fingerprints, scars, bruises or sensor condition should be given low weights. These conditions ensure accurate reconstruction of fingerprint orientation field for bad quality areas while preserving the true orientation field near singular points. The proposed algorithm has been evaluated on a publicly available database, FVC2004 DB1A. Experimental results reveal that it has better orientation field estimation compared to the various state of the art algorithms.

Iris biometrics is widely used because of highly discriminative characteristics that are found in iris. But designing an iris recognition system which is invariant to extrinsic factors such as illumination, noise, camera-to-eye distance is fairly challenging. This paper proposes a novel iris recognition approach which takes into account the iris structure, illumination variation, occlusion, noise and rotational variance. A feature extraction technique exploiting the local iris features has been proposed that uses two features extracted from different blocks of an iris. The features are extracted from suitably modified pixel values which are enhanced to improve robustness of the technique. Relational measure is proposed that considers both radial and circumferential features which is combined with the block local binary pattern (BLBP). The BLBP is applied in an unconventional block-wise manner adaptive to the iris structure. Finally scores are fused at score level. Experimental results on two publicly available Casia Interval and Lamp databases as well as on our IITK iris database demonstrates the usefulness of the proposed system.

This paper proposes a transaction strategy to implement transferable E-cash. It does not involve any third party observer at the time of transaction and possesses strong anonymity property. It uses biometric features of the legitimate holder of e-cash to progress the transaction. It works on the transfer of coin ownership and is built around the restrictive blinding and biometric digital signature scheme. Its mathematical model utilizes the hardness of discrete log problem and representation problem in groups. It guarantees anonymity and unlinkability for a genuine user and non repudiation to a fraudulent one.

This paper proposes a novel steganography technique, where edges in the cover image have been used to embed messages. Amount of data to be embedded plays an important role on the selection of edges, i.e., the more the amount of data to be embedded, larger the use of weaker edges for embedding. Experimental results have shown that the proposed technique performs better or at least at par with the state-of-the-art steganography techniques but provides higher embedding capacity.

This paper proposes an indexing technique for iris database using iris codes. Iris code which is the textural features of an iris is efficiently hashed such that it reduces the complexity of searching. It has been built on the Hamming distance based Locality Sensitive Hashing that samples bits of iris code. It reduces both computational and memory costs significantly. Also, it is robust to illumination and occlusion due to eyelids and eyelashes. It has been tested on publicly available database, viz. CASIA-V3-Interval [7]. Further, it has been compared with enhanced geometric hashing [9] technique and it is found to be better in terms of its query response time.

Image quality can play key role in the system performance. The recently introduced knuckleprint biometric has shown promising results, but its quality assessment is difficult because it lacks well defined and structured features as in the case of face or fingerprint. To our knowledge this is the first attempt to automatically assess the quality of knuckleprint images. The quality of knuckleprint images mainly depends upon the vertical line like features, focus, contrast and reflections produced by the camera flash. In this paper an effort has been made to identify, estimate and quantify some of these quality attributes and fuse them to obtain an overall quality score for any knuckleprint image. The largest publicly available PolyU knuckleprint database is used for testing, containing 7920 images. Extensive study is being carried out in order to establish a relationship between image quality and matching performance in order to demonstrate the proposed framework's utility.

This paper presents an enhanced geometric hashing technique suitable for object recognition. Unlike the available geometric hashing, the proposed technique needs less amount of time and memory, uniform index distribution in the hash space without using any rehashing function. It performs indexing and searching in one pass with linear complexity. The proposed technique has been applied in biometric databases. It has been tested for three traits such as ear, iris and palm print. The hitrate of 100% has been achieved for top 5 best matches in all cases.

This paper proposes a dissimilarity measure that can be used as a distance between two images. It has shown better discriminative power to recognize faces than similar existing variants for discriminating facial images. It gives more weight to the pixels which are often a part of the edge and counts pixels that are unmatched between query and database images. This measure has been tested on a publicly available database as ORL, YALE, CALTEC, BERN and also on a database developed at IIT Kanpur. Experimental results show that the proposed measure achieves a high recognition rate of 99.75% 93.75% 99.03% 98.93% 99.73% for the first likely matched faces on databases ORL, YALE, BERN, CALTECH, IITK respectively. The proposed measure can provide not only effective result against pose and expression variations but also against slight illumination variation.

This paper proposes an efficient retrieval technique which uses a new indexing scheme for a large palmprint database. For a given palmprint query image, the proposed indexing scheme makes use of match score vectors to determine an index and uses this index to reduce the search space of the database. Finally, the retrieval technique uses palmprint texture to find the top t best matches from the reduced search space. The proposed technique has been tested on publicly available palmprint database viz PolyU of 7752 images and is found to be an efficient technique based on score vector indexing scheme. The test reveals that hitrate of 98.01% is achieved for the bin-success rate of 98.28%. The proposed indexing scheme is found to be more powerful than any other known indexing scheme based on match score vectors.

This paper proposes a palmprint based verification system using low-order Zernike moments of palmprint sub-images. The Zernike moments of corresponding sub-images of live and enrolled palmprint are matched using Euclidean distance for verification. An efficient weighted fusion technique to fuse matching scores of the sub-images is proposed. The palmprint is extracted from the hand image acquired using a low cost flat bed scanner. The extraction is robust to hand translation and rotation on the scanner. The proposed system is also robust to occlusion and can verify user the features of from non-occluded region. The system is tested on IITK, PolyU and CASIA databases of size 549, 5,239 and 7,752 hand images respectively.

This paper proposes a new powerful distance measure called Normalized Unmatched Points (NUP). This measure can be used in a face recognition system to discriminate facial images. It works by counting the number of unmatched pixels between query and database images. A face recognition system has been proposed which makes use of this proposed distance measure for taking the decision on matching. This system has been tested on four publicly available databases, viz. ORL, YALE, BERN and CALTECH databases. Experimental results show that the proposed measure achieves recognition rates more than 98.66% for the first five likely matched faces. It is observed that the NUP distance measure performs better than other existing similar variants on these databases.

This paper presents a robust iris recognition system using local feature descriptor. The proposed biometric system accounts for two crucial issues. Firstly, iris texture is usually occluded by upper and lower eyelids. To handle this problem, a novel sector based normalisation is proposed. In this approach only non-occluded region is extracted by forming sectors of variable size. Secondly, texture features of iris transforms linearly due to illumination and position of these features changes due to rotation. For this purpose speeded up robust features (SURF) are found to be useful and invariant to transformations. The system is rigorously tested on database collected from three different sources i.e., BATH, CASIAV3 and IITK. Several local and global approaches have been compared with SURF. Experiments show that SURF outperforms other existing approaches in terms of accuracy and speed.

This paper presents an efficient technique for automatic ear detection from side face images. The proposed technique detects ear by exploiting its inherent structural details and is rotation, scale and shape invariant. It can detect ear without any training or assuming prior knowledge of the input image. The technique is based on connected component analysis of a graph constructed using the edge map of the image and is evaluated on a data set consisting of 2361 side face images collected at IIT Kanpur. Ear detection results are found to be very good and speak for the efficiency and robustness of the technique. To show the accuracy of the detection, detected ears are used for recognition and results are compared with the same obtained when ear cropping in done manually.

Solving a large sparse system of linear equations (SSLE)over the binary field is an important step in the general number field seive (GNFS) algorithm that is often used to factorize large integers used in the RSA algorithm. Block Lanczos method proposed by Montgomery is an efficient and reliable method for solving such systems. A number of implementations of Block Lanczos method on distributed systems have been proposed in past. This paper discusses the parallel Montgomery's Block Lanczos method for solving binary system of equations on shared multiprocessors(SMP). A simple experiment shows that the speed of convergence of this algorithm is dependent on a wise choice of initial guess for kick-starting the algorithm. A somewhat dense choice of the initial guess is suggested rather than a random choice for faster convergence. Devoid of any communication overheads of a loosely coupled system, the improved method gives good performance on SMP systems which can provide an able alternative to the more popular distributed systems. Details of implementation of this algorithm on SMP and experimental results are also provided in the paper.

This paper presents a novel face recognition technique with graph topology drawn on scale invariant feature transform (SIFT) features and is compared with all the available well known techniques on SIFT features, and elastic bunch graph matching (EBGM) technique drawn on gabor wavelet feature. IITK face database is used for evaluation purpose. Test results show that the proposed graph matching technique will be an appropriate one for face recognition. Finally, the test results have been compared with the results found on BANCA face database following MC protocol only.

This paper proposes a parallel associative memory (PAM) based face recognition system. An image is split into blocks which are processed in parallel by conventional associative memory. The combined output of these are used to discriminate between faces. Further, a parameter termed as run length count has been used to discriminate between similar faces. This approach helps to scale up associative memory to higher resolution images with more detailed features thus improving recognition performance. To analyze the efficiency and goodness of the proposed system the experiments have been performed on ORL face database. The proposed method out performed most of the existing methods.

A parallel algorithm to generate all the nPr distinct permutations of r items out of n distinct items is presented in this paper. The algorithm requires O([nPr/P ⌉rlog2r) units of time when P processors are available in the SIMD computer.