Face recognition in different subspaces - A comparative study

Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Among many approaches to the problem of face recognition, appearance-based subspace analysis still gives the most promising results. In this paper we study the three most popular appearance-based face recognition projection methods (PCA, LDA and ICA). All methods are tested in equal working conditions regarding preprocessing and algorithm implementation on the FERET data set with its standard tests. We also compare the ICA method with its whitening preprocess and find out that there is no significant difference between them. When we compare different projection with different metrics we found out that the LDA+COS combination is the most promising for all tasks. The L1 metric gives the best results in combination with PCA and ICA1, and COS is superior to any other metric when used with LDA and ICA2. Our results are compared to other studies and some discrepancies are pointed ou

Gender Effect on Face Recognition for a Large Longitudinal Database

Aging or gender variation can affect the face recognition performance dramatically. While most of the face recognition studies are focused on the variation of pose, illumination and expression, it is important to consider the influence of gender effect and how to design an effective matching framework. In this paper, we address these problems on a very large longitudinal database MORPH-II which contains 55,134 face images of 13,617 individuals. First, we consider four comprehensive experiments with different combination of gender distribution and subset size, including: 1) equal gender distribution; 2) a large highly unbalanced gender distribution; 3) consider different gender combinations, such as male only, female only, or mixed gender; and 4) the effect of subset size in terms of number of individuals. Second, we consider eight nearest neighbor distance metrics and also Support Vector Machine (SVM) for classifiers and test the effect of different classifiers. Last, we consider different fusion techniques for an effective matching framework to improve the recognition performance.Comment: This paper has been accepted by IEEE International Workshop on Information Forensics and Security (2018 WIFS

Face Recognition Methodologies Using Component Analysis: The Contemporary Affirmation of The Recent Literature

This paper explored the contemporary affirmation of the recent literature in the context of face recognition systems, a review motivated by contradictory claims in the literature. This paper shows how the relative performance of recent claims based on methodologies such as PCA and ICA, which are depend on the task statement. It then explores the space of each model acclaimed in recent literature. In the process, this paper verifies the results of many of the face recognition models in the literature, and relates them to each other and to this work

Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm

Biometric recognition systems have advanced significantly in the last decade and their use in specific applications will increase in the near future. The ability to conduct meaningful comparisons and assessments will be crucial to successful deployment and increasing biometric adoption. The best modality used as unimodal biometric systems are unable to fully address the problem of higher recognition rate. Multimodal biometric systems are able to mitigate some of the limitations encountered in unimodal biometric systems, such as non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof attacks, and performance. More reliable recognition accuracy and performance are achievable as different modalities were being combined together and different algorithms or techniques were being used. The work presented in this paper focuses on a bimodal biometric system using face and fingerprint. An image enhancement technique (histogram equalization) is used to enhance the face and fingerprint images. Salient features of the face and fingerprint were extracted using the Gabor filter technique. A dimensionality reduction technique was carried out on both images extracted features using a principal component analysis technique. A feature level fusion algorithm (Mahalanobis distance technique) is used to combine each unimodal feature together. The performance of the proposed approach is validated and is effective.Comment: Focused on extraction of feature from two different modalities (face and fingerprint) using Gabor filte

Facial feature representation and recognition

Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression representation and recognition have become a promising research area during recent years. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this dissertation, the fundamental techniques will be first reviewed, and the developments of the novel algorithms and theorems will be presented later. The objective of the proposed algorithm is to provide a reliable, fast, and integrated procedure to recognize either seven prototypical, emotion-specified expressions (e.g., happy, neutral, angry, disgust, fear, sad, and surprise in JAFFE database) or the action units in CohnKanade AU-coded facial expression image database. A new application area developed by the Infant COPE project is the recognition of neonatal facial expressions of pain (e.g., air puff, cry, friction, pain, and rest in Infant COPE database). It has been reported in medical literature that health care professionals have difficulty in distinguishing newborn\u27s facial expressions of pain from facial reactions of other stimuli. Since pain is a major indicator of medical problems and the quality of patient care depends on the quality of pain management, it is vital that the methods to be developed should accurately distinguish an infant\u27s signal of pain from a host of minor distress signal. The evaluation protocol used in the Infant COPE project considers two conditions: person-dependent and person-independent. The person-dependent means that some data of a subject are used for training and other data of the subject for testing. The person-independent means that the data of all subjects except one are used for training and this left-out one subject is used for testing. In this dissertation, both evaluation protocols are experimented. The Infant COPE research of neonatal pain classification is a first attempt at applying the state-of-the-art face recognition technologies to actual medical problems. The objective of Infant COPE project is to bypass these observational problems by developing a machine classification system to diagnose neonatal facial expressions of pain. Since assessment of pain by machine is based on pixel states, a machine classification system of pain will remain objective and will exploit the full spectrum of information available in a neonate\u27s facial expressions. Furthermore, it will be capable of monitoring neonate\u27s facial expressions when he/she is left unattended. Experimental results using the Infant COPE database and evaluation protocols indicate that the application of face classification techniques in pain assessment and management is a promising area of investigation. One of the challenging problems for building an automatic facial expression recognition system is how to automatically locate the principal facial parts since most existing algorithms capture the necessary face parts by cropping images manually. In this dissertation, two systems are developed to detect facial features, especially for eyes. The purpose is to develop a fast and reliable system to detect facial features automatically and correctly. By combining the proposed facial feature detection, the facial expression and neonatal pain recognition systems can be robust and efficient

Facial recognition techniques using SVM: A comparative analysis

This paper presents a literary review of facial recognition in 2D, which plays an important role in the life of the human being in terms of safety, work activity, etc. The focus is on the results obtained by some researchers with the application of feature extraction techniques, pattern classifiers, databases and their respective percentage of efficiency obtained. The objective is to determine efficient techniques that allow an optimal 2D facial recognition process, based on the quality of databases, feature extractors and pattern classifiers

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

A new feature extraction method based on clustering for face recognition

Part 13: Feature Extraction - MinimizationInternational audienceWhen solving a pattern classification problem, it is common to apply a feature extraction method as a pre-processing step, not only to reduce the computation complexity but also to obtain better classification performance by reducing the amount of irrelevant and redundant information in the data. In this study, we investigate a novel schema for linear feature extraction in classification problems. The method we have proposed is based on clustering technique to realize feature extraction. It focuses in identifying and transforming redundant information in the data. A new similarity measure-based trend analysis is devised to identify those features. The simulation results on face recognition show that the proposed method gives better or competitive results when compared to conventional unsupervised methods like PCA and ICA

Diagnosis of Esophagitis Based on Face Recognition Techniques

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared