Face recognition: A holistic approach review

Abstract

Face recognition has become more significant and relevant in recent years owing to its potential applications. Face recognition has far reaching benefits to corporations, the government and the greater society. Face recognition is basically identifying individuals by their faces. There are many face recognition approaches which are generally classified as feature based and holistic approaches. Presently there are a very small number of studies which compare both these approaches. There is a tremendous increase in face recognition research nowadays; primarily because of the various negative events taking place around the globe. With the increase in the number of proposed algorithms and techniques the survey and evaluation of these algorithms and techniques becomes more vital to provide a boost to the research activities. The primary aim of this paper is to provide a critical summary of the existing literature on human face recognition over the past decade with special reference to holistic approaches to face detection.

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

“…Face recognition is a technique that is used to recognise a person from his/her face that has been previously trained from a dataset [10]. Face recognition is one of the most efficient biometric techniques to identify a person [11], and has advantages compared to other biometric methods, such as identifying could be done without requiring action from the user, it has non-intrusive characteristics [12].…”

“…The face recognition process can be divided into two parts, the first part is image processing, which consists of obtaining facial images through scanning, image quality improvement, image cropping, image filtering, edge detection, and extracting features in images. The second part is a recognition technique consisting of artificial intelligence compiled by genetic algorithms and other approaches for facial recognition [10]. To summarise the process of the face recognition system, there are three steps taken; those are face detection, feature extraction, and classification, as shown in Fig.…”

“…Small features derived from pixels in the face image represent global information. These features are used as a reference to identify faces and represent variations between different face images; therefore the uniqueness of each can be identified [10]. The holistic-based approach can be divided into the statistical approach and artificial intelligent approach.…”

“…The holistic-based approach can be divided into the statistical approach and artificial intelligent approach. Examples of statistical approach are Principal Component Analysis (PCA), Eigenfaces and fisher face, and Linear Discriminant Analysis (LDA), while examples of artificial intelligent approach are Neural Network (NN), Support Vector Machine (SVM), Hidden Markov Model (HMM), Radial Basic Fuzzy (RBF), and Local Binary Pattern (LBP) [10], [12] 3) Hybrid based approach Hybrid based approach combines two or more approaches in order to get more effective results in recognising a face. By making a hybrid approach, deficiencies contained in one method can be overcome by another method [12].…”

Face recognition on mobile environment images using appearance based methods

“…Yüz tanıma problemi üzerine yapılan çalışmaların önemli bir kısmında hız ve performans verimliligi açısından görünüş tabanlı yöntemler [2] tercih edilmektedir. M.Turk ve A. Pentland [3] tarafından yapılan Temel Bileşen Analizi (TBA) yöntemine dayalı özyüzler (eigenfaces) yaklaşımı ile yüz tanıma bu alandaki ilk çalışmalardandır.…”