An Accurate System for Face Detection and Recognition

2018

There are several biometrics available like finger print, iris identification etc. But Facial recognition or detection is one of the biometric software applications that can identify an particular individual in an digital image. Face recognitions were used in many applications in the field of banking, passport office etc. But the problem in the face recognition is it cannot identify the person in the case of identical twins. So the algorithm called local binary patters were used to indentify the face in the case of identical twins because the LBP can describe well about the micro patterns present in the face.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds' avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical

IRJET, 2020

Image processing and facial analysis has been an important research field which aims to identify human faces and found its use in various different fields for examples in security. The reasons for this trend are; first it's the wide range of law enforcement applications and the second availability of feasible technologies after many years of research. Face recognition and detection is still a difficult challenge and there is no unique method that provides an effective solution to all situations face processing may encounter. In this paper a study on face recognition based on different algorithms is presented for extraction of features.

International Journal of Advanced Research in Computer Science, 2018

Facial recognition technology is the process for identifying or verifying a face from digital images. The need for face recognition has been of real importance with the development of modern society. Detection and recognition of faces has been on the rise worldwide owing the requirement for security for economic transactions, authorization, national safety and security and other important factors. The technology comprises of face detection, database creation and face recognition. This paper presents a new approach of face identification using LBP method and Haar-like features. The first step is face detection which is done using Haar cascade classifier. After detection, a face is saved in the database. Then the faces from the database are passed through the face recognition algorithm. The Local Binary Pattern Histogram (LBPH) method is used for face recognition. The performance of face detection can be seen to produce maximum error of 1.6%, 2.1% and 0.8% in case of Real-Time video, image file and video file respectively which may be considered accurate. The recognition algorithm produces maximum error of 0.4% which may be considered accurate as well.

Research in Computing Science, 2015

In this paper the implementation of the Local Binary Pattern algorithm for face recognition is presented using the partial information of the face, the main contribution of this work is that segmenting the parts of face (forehead, eyes, mouth) can make the recognition a person using only their eyes and getting a percentage of up to 69%, which considering the limited information provided a good success rate is obtained. In the test phase AR facedatabase it was used and using the method of Viola Jones face is located and segmented to obtain templates for each person and each part of his face and Euclidean distance was used for classification task. Because in a real application do not always have all the face of the person to identify the proposed system shows that you can get good results with partial information about it, in addition the results show that in the ranking 6 always provided the right person, which is also useful in real applications.

Face Detection and Recognition is an important area in the field of substantiation. Maintenance of records of students along with monitoring of class attendance is an area of administration that requires significant amount of time and efforts for management. Automated Attendance Management System performs the daily activities of attendance analysis, for which face recognition is an important aspect. The prevalent technique and methodologies for detecting and recognizing face like PCA-LDA, etc fail to overcome issues such as scaling, pose, illumination, variations, rotation, and occlusions. The proposed system provides features such as detection of faces, extraction of the features, detection of extracted features, analysis of students' attendance and monthly attendance report generation. The proposed system integrates techniques such as image contrasts, integral images, Ada-Boost, Haar-like features and cascading classifier for feature detection. Faces are recognized using advanced LBP using the database that contains images of students and is used to recognize student using the captured image. Better accuracy is attained in results and the system takes into account the changes that occurs in the face over the period of time.

Interaction between man and machine can be obtained by detecting and classifying faces. Less light conditions and noise are the basic challenges in face detection and categorization. LBP is the best technique for face classification and detection because LBP is a non-parametric descriptor which capably summarizes the local structure of images. Due to dependability of LBP, various LBP techniques are established as well as demonstrated for the improvement of face recognition performance. This technique is insensitive to noise and low contrasted faces. However, this technique is failed to retain small important features like sticker on forehead and moles on face which carry useful information. Layered local binary pattern is proposed in this paper to vanquish all the drawbacks. For computational convenience, the eight binary codes are transformed at each layer into a decimal number.

2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2014

Face recognition is one of the most important tasks in computer vision and biometrics where many algorithms have been developed. The Local Binary Pattern (LBP) has been proved to be effective for facial image representation and analysis, but it is too local to be robust. In this paper, we present an improved method for face recognition named Elongated Multi-Block Local Ternary Pattern (EMBLTP), which is based on Local Binary Pattern (LBP).The proposed method is tested on Yale face database and compared with different variants of LBP. Experimental results show that, the classification rate of the proposed method is appreciable.

This paper presents a survey on the recent use of Local Binary Patterns (LBPs) for face recognition. LBP is becoming a popular technique for face representation. It is a non-parametric kernel which summarizes the local spacial structure of an image and it is invariant to monotonic gray-scale transformations. This is a very interesting property in face recognition. In this paper, we describe the LBP technique and different approaches proposed in the literature to represent and to recognize faces. The most representatives are considered for experimental comparison on a common face authentication task. For that purpose, the XM2VTS and BANCA databases are used according to their respective experimental protocols.