A State of the art Survey on Face Recognition Methods

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

An Analysis of Face Recognition under Face Mask Occlusions

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.

Uncertainty and heuristics for underactuated hands: grasp pose selection based on the ppotential grasp robustness metric

The Potential Grasp Robustness (PGR) metric considers different states of the contact points, relaxing the requirement of all points being far from the friction cone boundary. The addition of new states for each contact point increases the computational complexity, which is combinatorial on the number of states and takes a long time for grasping configurations with large number of hand-object contacts. In this work we analyse the computational complexity of two recently proposed heuristics, which consider that: (i) the minimum number of contact points needed could be in two states and (ii) an analysis of grasp contact data provides the most common combinations of contact points that lead to an accurate estimation of PGR. For selecting grasp configurations, the PGR computation approach is not robust because assumes that measured forces at the contact points do not have uncertainty. In addition to the heuristics, we propose a new uncertainty based metric, the coefficient of variation of PGR. The grasp selection experiments show that the coefficient of variation provides similar results to the pose variation metric. The grasp selection that uses the uncertainty based computation of PGR find more stable contact points than the maximization of the conventional PGR. Article highlights Development of new heuristics for computation of grasp metrics of underactuated hands. Definition of uncertainty-based metrics for grasp se- lection Reduction of reality gap for physics-based grasping metrics of underactuated hands

Systematic Evaluation of Design Choices for Deep Facial Action Coding Across Pose

The performance of automated facial expression coding is improving steadily. Advances in deep learning techniques have been key to this success. While the advantage of modern deep learning techniques is clear, the contribution of critical design choices remains largely unknown, especially for facial action unit occurrence and intensity across pose. Using the The Facial Expression Recognition and Analysis 2017 (FERA 2017) database, which provides a common protocol to evaluate robustness to pose variation, we systematically evaluated design choices in pre-training, feature alignment, model size selection, and optimizer details. Informed by the findings, we developed an architecture that exceeds state-of-the-art on FERA 2017. The architecture achieved a 3.5% increase in F1 score for occurrence detection and a 5.8% increase in Intraclass Correlation (ICC) for intensity estimation. To evaluate the generalizability of the architecture to unseen poses and new dataset domains, we performed experiments across pose in FERA 2017 and across domains in Denver Intensity of Spontaneous Facial Action (DISFA) and the UNBC Pain Archive.

Dense Feature Based Face Recognition from Surveillance Video using Convolutional Neural Network

Face Recognition is a field of identifying the person from the facial features and has wide application range in security, human computer interactions, finance etc. In recent years, many researchers have developed different algorithms to identify the Faces from various illumination variations and Pose variation, but these two problems remain unsolved in Face Recognition (FR) field. The Local Binary Pattern (LBP) has already proved its robustness in illumination variation. This paper proposes a four-patch Local Binary Pattern based FR utilizing Convolutional Neural Network (CNN) for identifying the Facial images from various illumination conditions and Pose variation.

Novel BSSSO-Based Deep Convolutional Neural Network for Face Recognition with Multiple Disturbing Environments

Face recognition technology is presenting exciting opportunities, but its performance gets degraded because of several factors, like pose variation, partial occlusion, expression, illumination, biased data, etc. This paper proposes a novel bird search-based shuffled shepherd optimization algorithm (BSSSO), a meta-heuristic technique motivated by the intuition of animals and the social behavior of birds, for improving the performance of face recognition. The main intention behind the research is to establish an optimization-driven deep learning approach for recognizing face images with multiple disturbing environments. The developed model undergoes three main steps, namely, (a) Noise Removal, (b) Feature Extraction, and (c) Recognition. For the removal of noise, a type II fuzzy system and cuckoo search optimization algorithm (T2FCS) is used. The feature extraction is carried out using the CNN, and landmark enabled 3D morphable model (L3DMM) is utilized to efficiently fit a 3D face from a single uncontrolled image. The obtained features are subjected to Deep CNN for face recognition, wherein the training is performed using novel BSSSO. The experimental findings on standard datasets (LFW, UMB-DB, Extended Yale B database) prove the ability of the proposed model over the existing face recognition approaches.

Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots

Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of poses. Finally, we propose an optimization method to mitigate said negative effects by considering key pose samples in the recognition system’s set of known faces. The conducted experiments demonstrate that this optimized set of poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace.

Image Alignment in Pose Variations of Human Faces by using Corner Detection Method and its Application for PIFR System

The Major challenge in the recent face recognition techniques is to deal with pose variations during matching as facial image differences occurs due to motion/rotation in image, which is very large. The Pose Invariant Face Recognition is still an open area for developers to find solution. In this paper focus is on PIFR techniques and combined it with other algorithms for enhancing the results. Here we are using the Harris Corner Detection model along with Image alignment and Image tagging to get front face images. By generalization different tricks to handle the pose on face images has minimized the pose variation. On evaluating performance of the system, we have also calculate the Euler angle and their position change and according to it correct the pose variation. The results are in accordance with the expected lines.