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Journal ArticleDOI

Face Analysis Using Row and Correlation Based Local Directional Pattern

22 Oct 2020-Electronic Letters on Computer Vision and Image Analysis (Universitat Autonoma de Barcelona)-Vol. 19, Iss: 3, pp 55-70
TL;DR: The two feature vectors obtained by these methods concatenated to form a hybrid descriptor outperforms the other descriptors in face analysis and is carried out on benchmark databases.
Abstract: Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis.
Citations
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Book ChapterDOI
01 Jan 2022
TL;DR: In this paper , a Smart Attendance with Real Time Face Recognition (SARTFR) is proposed based on Viola Jones Algorithm and LBP methods for student detection and recognition for tracking student attendance.
Abstract: Face Recognition (FR) is a biometric technique that involves determining whether the image of a given person’s face matches any of the face images stored in a database. As a key attributes of biometric ratification, FR is widely utilized in different types of administration systems for video surveillance, computer human interface, indoor access systems, & network security. The proposed scheme is designed for student detection and recognition for tracking student attendance. As a result, Smart Attendance with Real Time Face Recognition (SARTFR) is a practical solution for day to day employee management activities. SARTFR is proposed based on Viola Jones Algorithm and LBP methods. SARTFR has got better results in terms of detection, recognition and tracking from the results.
References
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Journal ArticleDOI
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.

6,650 citations

01 Oct 2008
TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Abstract: Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

5,742 citations

Journal ArticleDOI
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

5,563 citations

Journal ArticleDOI
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

4,816 citations

01 Jan 1998

3,650 citations