scholarly journals Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 465
Author(s):  
Syeda Amna Rizwan ◽  
Ahmad Jalal ◽  
Munkhjargal Gochoo ◽  
Kibum Kim

The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification.

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU

2013 ◽  
Vol 22 (3) ◽  
pp. 033023
Author(s):  
Dejun Tang ◽  
Weishi Zhang ◽  
Xiaolu Qu ◽  
Dongqing Zhang

2006 ◽  
Vol 1 (4) ◽  
Author(s):  
Mohamed Abdel-Mottaleb ◽  
Mohammad H. Mahoor ◽  
A-Nasser Ansari

Author(s):  
Payal Bose ◽  
Samir Kumar Bandyopadhyay

Nowadays security became a major global issue. To manage the security issue and its risk, different kinds of biometric authentication are available. Face recognition is one of the most significant processes in this system. Since the face is the most important part of the body so the face recognition system is the most important in the biometric authentication. Sometimes a human face affected due to different kinds of skin problems, such as mole, scars, freckles, etc. Sometimes some parts of the face are missing due to some injuries. In this paper, the main aim is to detect a facial spots present in the face. The total work divided into three parts first, face and facial components are detected. The validation of checking facial parts is detected using the Convolution Neural Network (CNN). The second part is to find out the spot on the face based on Normalized Cross-Correlation and the third part is to check the kind of spot based on CNN. This process can detect a face under different lighting conditions very efficiently. In cosmetology, this work helps to detect the spots on the human face and its type which is very helpful in different surgical processes on the face.


2005 ◽  
Vol 26 (15) ◽  
pp. 2409-2423 ◽  
Author(s):  
Kwok-Wai Wan ◽  
Kin-Man Lam ◽  
Kit-Chong Ng

Author(s):  
Marcella Peter ◽  
Jacey-Lynn Minoi ◽  
Suriani Ab Rahman

This paper presents a modified kernel-based Active Shape Model for neutralizing and synthesizing facial expressions. In recent decades, facial identity and emotional studies have gained interest from researchers, especially in the works of integrating human emotions and machine learning to improve the current lifestyle. It is known that facial expressions are often associated with face recognition systems with poor recognition rate. In this research, a method of a modified kernel-based active shape model based on statistical-based approach is introduced to synthesize neutral (neutralize) expressions from expressional faces, with the aim to improve the face recognition rate. An experimental study was conducted using 3D geometric facial datasets to evaluate the proposed modified method. The experimental results have shown a significant improvement on the recognition rates.


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