scholarly journals A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 647 ◽  
Author(s):  
Khalil Khan ◽  
Muhammad Attique ◽  
Ikram Syed ◽  
Ghulam Sarwar ◽  
Muhammad Abeer Irfan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Face Image ◽  
Head Pose ◽  
Unified Framework ◽  
Age And Gender ◽  
Face Image Analysis ◽  
Probability Maps ◽  
Face Segmentation ◽  
End To End ◽  
And Gender

Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results.

scholarly journals A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 328 ◽  
Author(s):  
Khalil Khan ◽  
Muhammad Attique ◽  
Rehan Ullah Khan ◽  
Ikram Syed ◽  
Tae-Sun Chung
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Face Image ◽  
Deep Convolutional Neural Networks ◽  
Age And Gender ◽  
Face Image Analysis ◽  
Probability Maps ◽  
End To End ◽  
And Gender

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

scholarly journals Face Image Analysis in Mobile Biometric Accessibility Evaluations

2019 ◽  
Author(s):  
Barbara Corsetti ◽  
Raul Sanchez-Reillo ◽  
Richard M. Guest ◽  
Marco Santopietro
Keyword(s):  
Image Analysis ◽  
Face Image ◽  
Face Image Analysis

Face, Image, and Analysis

2010 ◽  
pp. 1-15
Author(s):  
Yu-Jin Zhang ◽  
Yu-Jin Zhang ◽  
J.L. Molina ◽  
R. Giordano ◽  
J. Bromley
Keyword(s):  
Image Analysis ◽  
Face Image ◽  
Research Topic ◽  
Point Of View ◽  
Research Development ◽  
Technical Point ◽  
Face Image Analysis ◽  
Human Faces ◽  
General Technical

Face image analysis, consisting of automatic investigation of images of (human) faces, is a hot research topic and a fruitful field. This introductory chapter discusses several aspects of the history and scope of face image analysis and provides an outline of research development publications of this domain. More prominently, different modules and some typical techniques for face image analysis are listed, explained, described, or summarized from a general technical point of view. One picture of the advancements and the front of this complex and prominent field is provided. Finally, several challenges and prominent development directions for the future are identified.

Comparing Ranking Methods for Tensor Components in Multilinear and Concurrent Subspace Analysis with Applications in Face Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550006 ◽  
Author(s):  
Tiene A. Filisbino ◽  
Gilson A. Giraldi ◽  
Carlos E. Thomaz
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Gait Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Image Databases ◽  
Subspace Analysis ◽  
Raw Data ◽  
Face Image Analysis

In the area of multi-dimensional image databases modeling, the multilinear principal component analysis (MPCA) and concurrent subspace analysis (CSA) approaches were independently proposed and applied for mining image databases. The former follows the classical principal component analysis (PCA) paradigm that centers the sample data before subspace learning. The CSA, on the other hand, performs the learning procedure using the raw data. Besides, the corresponding tensor components have been ranked in order to identify the principal tensor subspaces for separating sample groups for face image analysis and gait recognition. In this paper, we first demonstrate that if CSA receives centered input samples and we consider full projection matrices then the obtained solution is equal to the one generated by MPCA. Then, we consider the general problem of ranking tensor components. We examine the theoretical aspects of typical solutions in this field: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes; (c) Application of Fisher criterium. We discuss these solutions for tensor subspaces learned using centered data (MPCA) and raw data (CSA). In the experimental results we focus on tensor principal components selected by the mentioned techniques for face image analysis considering gender classification as well as reconstruction problems.

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