A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

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

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.

Automatic facial expression recognition is a research topic
with interesting applications in the field of human-computer interaction,
psychology and product marketing. The classification accuracy for an
automatic system which uses static images as input is however largely
limited by the image quality, lighting conditions and the orientation of
the depicted face. These problems can be partially overcome by using a
holistic model based approach called the Active Appearance Model. A
system will be described that can classify expressions from one of the
emotional categories joy, anger, sadness, surprise, fear and disgust with
remarkable accuracy. It is also able to detect smaller, local facial features
based on minimal muscular movements described by the Facial Action
Coding System (FACS). Finally, we show how the system can be used
for expression analysis and synthesis.

A model based method for automatic facial expression recognition

Systems that rely on Face Recognition (FR) biometric have gained great importance ever since terrorist threats imposed weakness among the implemented security systems. Other biometrics i.e., fingerprints or iris recognition is not trustworthy in such situations whereas FR is considered as a fine compromise. This survey illustrates different FR practices that laid foundations on the issue of partial occlusion dilemma where faces are disguised to cheat the security system. Occlusion refers to facade of the face image which can be due to sunglasses, hair or wrapping of facial image by scarf or other accessories. Efforts on FR in controlled settings have been in the picture for past several years; however identification under uncontrolled conditions like illumination, expression and partial occlusion is quite a matter of concern. Based on literature a classification is made in this paper to solve the recognition of face in the presence of partial occlusion. These methods are named as part based methods that make use of Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA), Non-negative Matrix Factorization (NMF), Local Non-negative Matrix Factorization (LNMF), Independent Component Analysis (ICA) and other variations. Feature based and fractal based methods consider features around eyes, nose or mouth region to be used in the recognition phase of algorithms. Furthermore the paper details the experiments and databases used by an assortment of authors to handle the problem of occlusion and the results obtained after performing diverse set of analysis. Lastly, a comparison of various techniques is shown in tabular format to give a precise overview of what different authors have already projected in this particular field.

A Survey: Face Recognition Techniques under Partial Occlusion

Automatic face recognition is one of the most emphasizing dilemmas in diverse of potential relevance like in different surveillance systems, security systems, authentication or verification of individual like criminals etc Adjoining of dynamic expression in face causes a broad range of discrepancies in recognition systems Facial expression not only exposes the sensation or passion of any person but can also be used to judge his/her mental views and psychosomatic aspects This paper is based on a complete survey of face recognition conducted under varying facial expressions In order to analyze different techniques, motion-based, model-based and muscles-based approaches have been used in order to handle the facial expression and recognition catastrophe The analysis has been completed by evaluating various existing algorithms while comparing their results in general It also expands the scope for other researchers for answering the question of effectively dealing with such problems

Analysis of Face Recognition under Varying Facial Expression: A Survey

http://www.scielo.org.mx/pdf/jart/v13n1/v13n1a9.pdf

Robust Face Recognition Technique under Varying Illumination

Selecting a low dimensional feature subspace from thousands of features is a key phenomenon for optimal classification. Linear Discriminant Analysis (LDA) is a basic well recognized supervised classifier that is effectively employed for classification. However, two problems arise in intra class during discriminant analysis. Firstly, in training phase the number of samples in intra class is smaller than the dimensionality of the sample which makes LDA unstable. The other is high computational cost due to redundant and irrelevant data points in intra class. An Adaptive Margin Fisher's Criterion Linear Discriminant Analysis (AMFC/LDA) is proposed that a ddresses these issues and overcomes the limitations of intra class problems. Small Sample Size (SSS) problem is resolved through modified Maximum Margin Criterion (MMC), which is a form of customized LDA and convex hull. Inter class is defined using LDA while intra class is formulated using quick hull respectively. Similarly, computational cost is reduced by reformulating within class scatter matrix through minimum Redundancy Maximum Relevance (mRMR) algorithm while preserving discriminant information. The proposed algorithm reveals encouraging performance. Finally, a comparison is made with existing approaches.

Face Recognition Using Adaptive Margin Fisher's Criterion and Linear Discriminant Analysis (AMFC-LDA)

Within the diverse field of image processing, facial expression detection is tremendously an interesting part of face recognition. In order to track and locate facial expressions unusual parts of face movements like eyes, nose, mouth, cheeks etc must be observed. In this paper, six basic facial expressions are detected using Six Facial Expressions Hexagon (SFEH) Model. The SFEH model provides the general representation of six facial expressions on six edges of surface hexagon (S-Hex). The S-hex is the outer boundary of face and can be classified into three parts as upper triangle, middle rectangle and lower triangle. The partitioning of facial features into three parts narrows down the processing area of face where expression originates and is very helpful to locate the varied region. Moreover, Facial Action Coding System (FACS) and Facial Animation Parameters System (FAPS) are used as intermediate frame to analyze the proposed SFEH model. The scope of the proposed SFEH model is to customize only the varied region of the face using morphological operations under reasonable computational cost

Marryam Murtaza

Papers

A Survey: Face Recognition Techniques under Partial Occlusion

Analysis of Face Recognition under Varying Facial Expression: A Survey

Robust Face Recognition Technique under Varying Illumination

Face Recognition Using Adaptive Margin Fisher's Criterion and Linear Discriminant Analysis (AMFC-LDA)

Facial expression detection using Six Facial Expressions Hexagon (SFEH) model