Dakshina Ranjan Kisku

SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions

Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT) as feature descriptor for structural representation of ear images. In order to make it more robust to user authentication, only the regions having color probabilities in a certain ranges are considered for invariant SIFT feature extraction, where the K-L divergence is used for keeping color consistency. Ear skin color model is formed by Gaussian mixture model and clustering the ear color pattern using vector quantization. Finally, K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference model and probe ear images. After segmentation of ear images in some color slice regions, SIFT keypoints are extracted and an augmented vector of extracted SIFT features are created for matching, which is accomplished between a pair of reference model and probe ear images. The proposed technique has been tested on the IITK Ear database and the experimental results show improvements in recognition accuracy while invariant features are extracted from color slice regions to maintain the robustness of the system.

Multisensor Biometric Evidence Fusion for Person Authentication Using Wavelet Decomposition and Monotonic-Decreasing Graph

This paper presents a novel biometric sensor generated evidence fusion of face and palmprint images using wavelet decomposition for personnel identity verification. The approach of biometric image fusion at sensor level refers to a process that fuses multispectral images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce a new fused image in spatially enhanced form. When the fused image is ready for further processing, SIFT operator are then used for feature extraction and the recognition is performed by adjustable structural graph matching between a pair of fused images by searching corresponding points using recursive descent tree traversal approach. The experimental result shows the efficacy of the proposed method with 98.19% accuracy, outperforms other methods when it is compared with uni-modal face and palmprint authentication results with recognition rates 89.04% and 92.17%, respectively and when all the methods are processed in the same feature space.

Biometric sensor image fusion for identity verification: A case study with wavelet-based fusion rules graph matching

Multibiometric systems have many advantages over the uni-biometric systems. However, multibiometric systems lacking in many respects, such as multimodal systems not only acquire relevant and viable information for fusion, but also acquire some irrelevant and redundant information which are associated to the feature sets or with the match score sets, and this may lead to the resultant performance to be degraded. This paper deals with a biometric authentication system that uses image fusion convention for face and palmprint images using wavelet decomposition. The proposed work uses a few selected wavelet fusion rules subject to fusion of biometric face and palmprint images at low-level. While fusion is accomplished with two high-resolution biometric images, SIFT operator is used to extract invariant features from spatially enhanced fused image. Finally, identity is verified by probabilistic relational graph with posteriori attributes matching between a pair of fused images. Matching is employed by searching corresponding feature points in both the database and query fused images using the iterative relaxation algorithm. The experimental results show that the proposed multimodal biometric system through image fusion outperforms feature level fusion methods, while all the fusion schemes are implemented in the same feature space, i.e., in the scale invariant feature space.

Multispectral Palm Image Fusion for Person Authentication Using Ant Colony Optimization

This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, fused image is convolved with Gabor wavelet transform. The Gabor wavelet feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony optimization algorithm is applied to select relevant, distinctive and reduced feature set from Gabor responses. Finally, the reduced set of features is trained with support vector machines and accomplished user recognition tasks. For evaluation, CASIA multispectral palmprint database is used. The experimental results reveal that the system is found to be robust and encouraging while variations of classifiers are used. Also a comparative study is presented of the proposed system with a well-known method.

Offline signature verification using geometric and orientation features with multiple experts fusion

This paper reports a weighted fusion of multiple classifiers for offline signature verification using geometric and orientation features. The proposed system uses three different classifiers for identity verification, namely, Gaussian empirical rule, Mahalanobis and Euclidean distance metrics. Initially, Geometric global and local features are extracted from signature image. Further, a novel feature extraction technique is applied to signature image for extraction of orientation features. These feature sets are then fused and make a concatenated feature set which is then passed through the three classifiers. Matching scores obtained from these three classifiers are finally fused using weighted sum rule. The proposed system is tested on IIT Kanpur signature database which consists of 1800 offline signatures. The experimental results are found to be convincing and encouraging. The aim of the proposed system is to provide such a system which can overcome the problem of skilled forgery detection efficiently with less computational complexity.

Graph application on face for personal authentication and recognition

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.

Feature level fusion of face and palmprint biometrics by isomorphic graph-based improved K-medoids partitioning

This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clusters. By partitioning the face and palmprint images with scale invariant features SIFT points, a number of clusters is formed on both the images. Then on each cluster, an isomorphic graph is drawn. In the next step, the most probable pair of graphs is searched using iterative relaxation algorithm from all possible isomorphic graphs for a pair of corresponding face and palmprint images. Finally, graphs are fused by pairing the isomorphic graphs into augmented groups in terms of addition of invariant SIFT points and in terms of combining pair of keypoint descriptors by concatenation rule. Experimental results obtained from the extensive evaluation show that the proposed feature level fusion with the improved K-medoids partitioning algorithm increases the performance of the system with utmost level of accuracy.

Multibiometrics Belief Fusion

This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on multimodal database containing face and ear images of 400 individuals. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.

Data Hiding in Images Using Some Efficient Steganography Techniques

Steganography is the art of hiding data in a seemingly innocuous cover medium. For example – any sensitive data can be hidden inside a digital image. Steganography provides better security than cryptography because cryptography hides the contents of the message but not the existence of the message. So no one apart from the authorized sender and receiver will be aware of the existence of the secret data. Steganographic messages are often first encrypted by some traditional means and then a cover image is modified in some way to contain the encrypted message. The detection of steganographically encoded packages is called steganalysis. In this paper, we propose three efficient Steganography techniques that are used for hiding secret messages. They are LSB based Steganography, Steganography using the last two significant bits and Steganography using diagonal pixels of the image. Symmetric and asymmetric key cryptography has been used to encrypt the message.

An Efficient Ear Identification System

This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. It makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are extracted from each color slice region as a part of invariant feature extraction. The extracted keypoints are then fused separately by the two fusion approaches, namely concatenation and the Dempster-Shafer theory. Finally, the fusion approaches generate two independent augmented feature vectors for identification of individuals separately. The proposed technique is tested on IIT Kanpur ear database of 400 individuals and is found to achieve 98.25% accuracy for identification of top 5 best matches.