Facial expression recognition with Convolutional Neural Networks

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.

Facial Expression Analysis under Partial Occlusion: A Survey

A survey on deep learning based face recognition

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Handbook Of Biometrics

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.

Face recognition is being widely accepted as a biometric technique because of its non-intrusive nature. Despite extensive research on 2-D face recognition, it suffers from poor recognition rate due to pose, illumination, expression, ageing, makeup variations and occlusions. In recent years, the research focus has shifted toward face recognition using 3-D facial surface and shape which represent more discriminating features by the virtue of increased dimensionality. This paper presents an extensive survey of recent 3-D face recognition techniques in terms of feature detection, classifiers as well as published algorithms that address expression and occlusion variation challenges followed by our critical comments on the published work. It also summarizes remarkable 3-D face databases and their features used for performance evaluation. Finally we suggest vital steps of a robust 3-D face recognition system based on the surveyed work and identify a few possible directions for research in this area.

3-D face recognition: features, databases, algorithms and challenges

Expression invariant face recognition using local binary patterns and contourlet transform

The rapid growth and technological progressions in the vehicle edge computing and networks (VECON) enhanced existing vehicular administrations through information sharing and information investigation, which further incremented traffic security difficulties. This carries the need to verify vehicle networks with gigantic information stockpiling substances. Recognizing the vehicles that communicate fashioned messages and ensuring the protection of every vehicle is an essential assignment. Blockchain can be utilized as an effective solution to provide security during vehicle edge computing. Every transaction and data is recorded in the blockchain, which expands the number of blocks after some time. Another test in blockchain methods is utilizing a consensus mechanism, which can be effectively undermined by the attackers. Artificial intelligence (AI) trained by machine learning (ML) algorithms as an amazing paradigm can be incorporated with blockchain to settle these issues. The data storing methods of blockchain can likewise be enhanced with the assistance of ML algorithms. In this paper, a two-tier authenticated consortium blockchain (TTA-CB) protocol is proposed for secure information partaking in Vehicle Edge Computing and Networks (VECONs). Utilizing a one-time password (OTP) based notoriety estimation calculation, the misbehaving vehicles are recognized. The use of Particle Swarm Optimization (PSO) understands the ideal data provider selection issue utilizing notoriety esteems. Exploratory outcomes demonstrate the importance of the proposed strategy, and the correlation results demonstrate that the proposed technique is unrivaled and secure.

An integrative approach for secure data sharing in vehicular edge computing using Blockchain

In biometrics, Fingerprint is widely used in identification of individual’s identity. Biometric recognition is leading technology for identification and security systems. Fingerprint has unique identification among all other biometric modalities.Use of the fingerprints as biometric characteristics is extensively used and developed for fingerprint recognition in forensic, civilian and commercial applications.This paper presents the brief data about fingerprint spoofing which encompasses misuse caused by the attackers. Fingerprint spoofing detection attributes to the investigation of the finger characteristics to ensure whether the finger is spoofed or live. The various spoofing types are explained and there detection techniques are introduced with three commonly used databases. General Terms Image processing, Machinelearning, Fingerprint Recognition.

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Survey on Fingerprint Spoofing, Detection Techniques and Databases

This paper addresses the issue of human face recognition in presence of expression variations, which pose a great challenge to face recognition systems. Typically, the discriminant features lie in both spatial as well as transform domain. In this paper, we propose combination of Discrete Wavelet Transform (DWT) and proposed Semi-decimated Discrete Wavelet Transform (SDWT) to develop an expression invariant face recognition algorithm followed by a novel wavelet coefficients enhancement function. The wavelet coefficients are boosted using the proposed coefficients enhancement function and extracted using the Weber Local Descriptors (WLD). This enhances weak skin edges based features, resulting in increased probability of recognition. The proposed algorithm also exploits spatial domain features using our customized version of Complete Local binary patterns (CLBP) named Patch Local Difference Sign Magnitude Transform (Patch-LDSMT) applied on complete images and physiologically meaningful overlapping regions of human facial images for the first time. Feature level fusion of the wavelet based features and Patch-LDSMT yields a robust feature vector whose dimensionality is reduced using Linear Discriminant Analysis (LDA). Comprehensive experimentation is carried out on the JAFFE, CMU-AMP, ORL, Yale, Cohn-Kanade (CK) and database collected by us. Benchmarking analysis illustrates that the proposed face recognition algorithm offers much better rank one recognition performance when compared with the current state-of-the-art expression invariant face recognition approaches.

Hemprasad Y. Patil

Papers

3-D face recognition: features, databases, algorithms and challenges

Expression invariant face recognition using local binary patterns and contourlet transform

An integrative approach for secure data sharing in vehicular edge computing using Blockchain

Survey on Fingerprint Spoofing, Detection Techniques and Databases

Expression invariant face recognition using semidecimated DWT, Patch-LDSMT, feature and score level fusion