Soumendu Chakraborty

Local directional gradient pattern: a local descriptor for face recognition

In this paper a local pattern descriptor in high order derivative space is proposed for face recognition. The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between higher order derivatives of the reference pixel in four distinct directions. The proposed descriptor identifies relationship between the high order derivatives of the referenced pixel in four different directions to compute the micropattern which corresponds to the local feature. Proposed descriptor considerably reduces the length of the micropattern which consequently reduces the extraction time and matching time while maintaining the recognition rate. Results of the extensive experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate of the descriptor is almost similar to existing state of the art methods. Moreover the proposed descriptor is more resistant against the AWGN compared to the other state of the art descriptors used for face recognition problems.

LSB based non blind predictive edge adaptive image steganography

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.

Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval

Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination, and lighting conditions. The accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper, a local gradient hexa pattern is proposed that identifies the relationship among the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. The proposed descriptor effectively transforms these relationships into binary micropatterns discriminating inter-class facial images with optimal precision. The recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors, namely, local derivative pattern, local tetra pattern, multiblock local binary pattern, and local vector pattern over the most challenging and benchmark facial image databases, i.e., Cropped Extended Yale B, CMU-PIE, color-FERET, LFW, and Ghallager database. The proposed descriptor has better recognition as well as retrieval rates compared with state-of-the-art descriptors.

Local quadruple pattern

The proposed descriptor is a new descriptor, which explores circular asymmetry of a facial image.The proposed descriptor effectively encodes the local neighborhood with two patterns of eight bits each, which is very less compared to the length of the micropattern encoded using LDP, and LVP.The descriptor shows consistent improvements in recognition as well as retrieval frameworks.Comparative test results show that the descriptor outperforms state of the art descriptors such as LBP, SLBP, CSLBP, CSLTP, LVP, LDP and LDGP.The performance of the proposed descriptor is even better in uncontrolled environment. In this paper a novel hand-crafted local quadruple pattern (LQPAT) is proposed for facial image recognition and retrieval. Most of the existing hand-crafted descriptors encode only a limited number of pixels in the local neighborhood. Under unconstrained environment the performance of these descriptors tends to degrade drastically. Major problem in increasing the local neighborhood is that, it also increases the feature length of the descriptor. The proposed descriptor tries to overcome these problems by defining an efficient encoding structure with optimal feature length. The proposed descriptor encodes relations amongst the neighbors in quadruple space. Two micro patterns are computed from the local relationships to form the descriptor. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand crafted descriptors on bench mark databases namely; Caltech-face, LFW, Color-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under uncontrolled variations.

Secret image sharing using grayscale payload decomposition and irreversible image steganography

To provide an added security level most of the existing reversible as well as irreversible image steganography schemes emphasize on encrypting the secret image (payload) before embedding it to the cover image. The complexity of encryption for a large payload where the embedding algorithm itself is complex may adversely affect the steganographic system. Schemes that can induce same level of distortion, as any standard encryption technique with lower computational complexity, can improve the performance of stego systems. In this paper, we propose a secure secret image sharing scheme, which bears minimal computational complexity. The proposed scheme, as a replacement for encryption, diversifies the payload into different matrices which are embedded into carrier image (cover image) using bit X-OR operation. A payload is a grayscale image which is divided into frequency matrix, error matrix, and sign matrix. The frequency matrix is scaled down using a mapping algorithm to produce Down Scaled Frequency (DSF) matrix. The DSF matrix, error matrix, and sign matrix are then embedded in different cover images using bit X-OR operation between the bit planes of the matrices and respective cover images. Analysis of the proposed scheme shows that it effectively camouflages the payload with minimum computation time.

An efficient bit plane X-OR algorithm for irreversible image steganography

The science of hiding secret information in another message is known as Steganography; hence the presence of secret information is concealed. It is the method of hiding cognitive content in same or another media to avoid recognition by the intruders. This paper introduces new method wherein irreversible steganography is used to hide an image in the same medium so that the secret data is masked. The secret image is known as payload and the carrier is known as cover image. X-OR operation is used amongst mid level bit planes of carrier image and high level bit planes of data image to generate new low level bit planes of the stego image. Recovery process includes the X-ORing of low level bit planes and mid level bit planes of the stego image. Based on the result of the recovery, subsequent data image is generated. A RGB color image is used as carrier and the data image is a grayscale image of dimensions less than or equal to the dimensions of the carrier image. The proposed method greatly increases the embedding capacity without significantly decreasing the PSNR value.

High capacity secret image sharing using multilayer image steganography with primary cover predictive error

Image steganography is the art of hiding secret message in greyscale or cover images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system two tier data hiding is used. In this paper, we propose a high capacity image steganography with greyscale primary cover. Predictive primary cover PPC is computed from the primary cover using median edge detector MED predictor. Predictive error PE is the difference between primary cover and the PPC. The high level bit planes of the secret image payload are X-ORed with low level bit planes of the PPC and vice-verse. The resulting Stego image is embedded in a secondary cover image using bit plane X-OR algorithm. The cover image used is an RGB colour image. The proposed steganography scheme enhances the level of security of the existing bit plane X-OR algorithm without considerably increasing the computation time and detectability. The experimental results show that the proposed scheme achieves higher level of security with high embedding capacity. Higher level of security achieved by the proposed multilayer steganography scheme enhances the performance and security of the bit plane X-OR algorithm.

Correction to: Local directional gradient pattern: a local descriptor for face recognition

The Eqs. 3 and 15 in the original version of this article contained an error.

Centre symmetric quadruple pattern: A novel descriptor for facial image recognition and retrieval

Abstract Facial features are defined as the local relationships that exist amongst the pixels of a facial image. Hand-crafted descriptors identify the relationships of the pixels in the local neighborhood defined by the kernel. Kernel is a two dimensional matrix which is moved across the facial image. Distinctive information captured by the kernel with limited number of pixel achieves satisfactory recognition and retrieval accuracies on facial images taken under constrained environment (controlled variations in light, pose, expressions, and background). To achieve similar accuracies under unconstrained environment local neighborhood has to be increased, in order to encode more pixels. Increasing local neighborhood also increases the feature length of the descriptor. In this paper we propose a hand-crafted descriptor namely Centre Symmetric Quadruple Pattern (CSQP), which is structurally symmetric and encodes the facial asymmetry in quadruple space. The proposed descriptor efficiently encodes larger neighborhood with optimal number of binary bits. It has been shown using average entropy, computed over feature images encoded with the proposed descriptor, that the CSQP captures more meaningful information as compared to state of the art descriptors. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand-crafted descriptors (CSLBP, CSLTP, LDP, LBP, SLBP and LDGP) on bench mark databases namely; LFW, Color-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under controlled as well as uncontrolled variations in pose, illumination, background and expressions.

Performance enhancement of local vector pattern with generalized distance local binary pattern for face recognition

In this paper a generalized distance based local binary pattern (GDLBP) is proposed which achieves better recognition accuracy over LBP and significantly increases the recognition accuracy when combined with LVP. The proposed GDLBP is combined with local vector pattern (LVP) to achieve better recognition rates. Proposed GDLBP explores the spatial relationships of the neighboring pixels at different radii and inter radii distances. Proposed descriptors GDLBP and GDLBP-LVP have been analyzed and compared with the existing LBP and LVP over most challenging benchmark facial image databases LFW, and color FERET. Analysis of the descriptors shows that the proposed GDLBP and GDLBP-LVP achieves noticeably better results as compared to LBP and LVP.