Local Directional Pattern (LDP) is a descriptor used for face recognition. It assigns a code for each pixel in the image, and the resultant LDP-encoded image is divided into regions for which each a histogram is generated. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDP, a dimensionality reduced local directional pattern (DR-LDP) is proposed in this paper. The proposed descriptor computes single code for each block by X-ORing the LDP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDP coding pattern constraints. The significance of DR-LDP is the compact code generation for efficient face recognition. The experiments were carried out on standard databases like FERET, extended YALE-B database and ORL. The resultant DR-LDP descriptor provided better recognition rates, outperforming the existing local descriptor-based methods and proving its efficacy. The compact code can be further extended to provide biometric security. © 2016 Elsevier Ltd

Srinivasa Perumal R

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Chandra Mouli P.V.S.S.R.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Expert Systems with Applications

Biometrics is an emerging research area due to its easiness in identification of the person. Face Spoofing is the challenging task in face recognition systems because the human can easily trickster the system by presenting the video or photograph of the person. Many approaches are providing good results in face spoofing, but still it is challenging in intra and cross database validation. Deep learning algorithms have shown significant results in the intra and cross database. This paper used deep learning for extracting the inclusive and favorable features of the person from the face. The extracted features are used for classifying the face image as a real face or genuine face. The performance of the method is evaluated through statistical measures. The experiments were carried out NUAA and CASIA database. The method attained most promising results than other face spoofing methods. © 2019, Springer Nature Singapore Pte Ltd.

Communications in Computer and Information Science Recent Trends in Image Processing and Pattern Recognition

Learning Deep Feature Representation for Face Spoofing

Face analysis is an emerging area in biometric research. This paper proposed the robust local descriptor using deep learning for face analysis. Recently the researchers used the adaptive filter approach to enhance the image instead of using the static filter approach. Deep learning helps to create an adaptive filter and it achieved significant results on face recognition. This motivated us to use deep learning concepts to recognize the face. In this paper, dimensionality-reduced local directional pattern descriptor is used to enhance the face image. PCANet model is applied over the resultant encoded image to discriminate the features of face the image that is used for identification and classification of the face image. The experiment was carried out with the standard benchmark databases such as FERET, Extended Yale B, LFW, ORL and AR databases. This method achieved better recognition rate compared with state-of-the-art methods. © 2017, Springer-Verlag London Ltd., part of Springer Nature.

International Journal of Multimedia Information Retrieval

Robustness of DR-LDP over PCANet for face analysis

Face recognition and facial expression recognition using local patterns is the order of the day. Local directional number pattern (LDNP) is one of the prominent descriptor for face recognition. LDNP assigns a 3 bit code for each pixel in the image. The resultant LDNP labeled image is divided into regions to form histogram based descriptor. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDNP, a dimensionality reduced local directional number pattern (DR-LDNP) is proposed in this paper. The proposed descriptor computes single code for each block. This is done by X-ORing of the LDNP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDNP coding pattern constraints. The resultant DR-LDNP descriptor outperforms the existing methods. The experimentation is carried out on standard databases like FERET, YALE, ORL, Cohn–Kannade and JAFFEE and obtained good recognition rates compared to other methods. © 2016, Springer-Verlag Berlin Heidelberg.

Journal of Ambient Intelligence and Humanized Computing

Dimensionality reduced local directional number pattern for face recognition

Face recognition has lot of challenges in biometrics. The challenges are addressed effectively by local pattern descriptors. The idea of local descriptors is to determine the feature vector and then compute the difference between test images with training images by using similarity measure. Based on the observation, local approaches attained better performance rate than other approaches in face recognition. Due to that, researchers made a significant attention on local descriptors for face recognition. For nonlinear subspace, the local descriptors will achieve better result than holistic approach. Local pattern descriptor follows simple procedure to extract the facial features. The steps are face alignment, face representation, and matching. Face alignment is the first step of local descriptor, which is used to divide the image into several blocks. Face representation is used to extract the meaningful information from each region. This local feature extraction method carries discriminant information of the region; it will improve the classification rate and matching rate. Local descriptors extract the discriminant information from the neighbors by setting a threshold value as center pixel value, and it is not capable of extracting the detailed information from microstructure. Finally, matching by classification techniques or distance measure is used to identify or verify the person. The local pattern descriptors are more robust against pose, lighting, and scale variations. This chapter describes the various local pattern descriptors and shows the effectiveness of the descriptor. The results of local pattern descriptors are experimented on standard benchmark databases such as FERET, Extended Yale-B, ORL, CAS-PEAL, LFW, JAFFE, and Cohn-Kannade. © Springer Nature Singapore Pte Ltd. 2018. All rights reserved.

Knowledge Computing and its Applications

A Comparative Analysis of Local Pattern Descriptors for Face Recognition

This paper affords an analysis of a novel local descriptor-dimensionality reduced local directional Pattern (DR-LDP) on different scales. DR-LDP extracts the features of the face by partitioning the image into 3 × 3 sub-regions and the sub-region was convoluted with a set of eight Kirsch masks. The single eight-bit code generated for each sub-region. The histogram features are extracted by partitioning the resultant DR-LDP encoded image into 8 × 8 regions. The features of each regions are combined to form a feature vector for the given facial image. For any query image, the same process is carried out to extract the feature vector. A chi-square test is used to measure the dissimilarity of the feature, the dissimilarity of feature vector in the database with the feature vector of query image was determined to recognize the face. The experiments had been accomplished on well-known benchmark databases. In this paper, an analysis of DR-LDP on 3 × 3, 5 × 5, 7 × 7 regions and convultes each region with 3 × 3, 5 × 5, 7 × 7 eight Kirsch masks are performed to test the robustness of it. From the analysis, it is evident that the DR-LDP performs the best for the scale 3 × 3. © 2017 IEEE.

Journal	Data powered by TypesetExpert Systems with Applications
Publisher	Data powered by TypesetElsevier BV
ISSN	0957-4174
Open Access	No