Future of Face Recognition: A Review

Face recognition has become an attractive field in computer based application development in the last few decades. That is because of the wide range of areas they used in. And because of the wide variations of faces, face recognition from the database images, real data, capture images and sensor images is challenging problem and limitation. Image processing, pattern recognition and computer vision are relevant subjects to face recognition field. The innovation of new approaches of face authentication technologies is continuous subject to build much strong face recognition algorithms. In this work, to identify a face, there are three major strategies for feature extractions are discussed. Appearance-based and Modelbased methods and hybrid techniques as feature extractions are discussed. Also, review of major person recognition research the characteristics of good face authentication applications, Classification, Distance measurements and face databases are discussed while the final suggested methods are presented. This research has six sections organized as follow: Section one is the introduction. Section two is dedicated to applications related to face recognition. In Section three, face recognition techniques are presented by details. Then, classification types are illustrated in Section four. In section five, standard face databases are presented. Finally, in Section six, the conclusion is presented followed by the list of references.

A Review of Person Recognition Based on Face Model

During the last few years, Local Binary Patterns (LBP) has aroused increasing interest in image processing and computer vision. LBP was originally proposed for texture analysis, and has proved a simple yet powerful approach to describe local structures. It has been extensively exploited in many applications, for instance, face image analysis, image and video retrieval, environment modeling, visual inspection, motion analysis, biomedical and aerial image analysis, remote sensing. Face recognition is an interesting and challenging problem, and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access, and personal identification among others. In this paper we are concerned with face recognition in a video stream using Local Binary Pattern histogram with processed data. First we will detect faces by using a combination of Haar cascade files that uses skin detection, eye detection and nose detection as input of LBP to increase the accuracy of the proposed recognition system. Also, our system can be used to build a dataset of faces and names to be used in a recognition step. The experimental results have shown that the proposed system can achieve accuracy of recognition up to 96.5% which was better than the relevant methods.

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An Accurate System for Face Detection and Recognition

In recent years, deep learning has become a very prevalent technology in face recognition. Google came up with a deep convolution neural network called Facenet which performs face recognition using only 128 bytes per face. As claimed by Google, Facenet attained nearly 100-percent accuracy on the widely used Labeled Faces in the Wild (LFW) dataset. But in the case of low resolution face images it’s the other way round. This low resolution challenge occurs in many existing face recognition algorithms, due to which satisfactory performance has become hard to be achieved. The goal of this paper is to present the obtained results after evaluating the performance of Facenet on low resolution face images compared to high resolution face images.

Performance Evaluation of Facenet on Low Resolution Face Images

In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

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Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

At present face recognition has wide area of applications such as security, law enforcement. Imaging conditions, Orientation, Pose and presence of occlusion are huge problems associated with face recognition. The performance of face recognition systems decreases due to these problems. Discriminant Analysis (LDA) or Principal Components Analysis (PCA) is used to get better recognition results. Human face contains relevant information that can extracted from face model developed by PCA technique. Principal Components Analysis method uses eigenface approach to describe face image variation. A face recognition technique that is robust to all situations is not available. Some techniques are better in case of illumination, some for pose problem and some for occlusion problem. This paper presents some algorithms for face recognition.

Study of Different Face Recognition Algorithms and Challenges

This paper presents a face completion and recognition algorithm under partial occlusion. Componentbased algorithm utilized in the proposed method. Individual components are detected using Haar-like features and wild card estimation is used for face completion task. The system performs better results under partial occlusion conditions in comparison to existing methods. AR face database is used for experiments.

R. K. Baghel

Papers

Study of Different Face Recognition Algorithms and Challenges

Partially Occluded Face Recognition and Components Estimation