CLASSIFIERS SENSITIVITY FOR BOUNDARY CASE TESTING SET IN THE FACE RECOGNITION ALGORITHM BASED ON THE ACTIVE SHAPE MODEL

2009 ◽  
Keyword(s):  
Face Recognition ◽  
Recognition Algorithm ◽  
Active Shape Model ◽  
Shape Model ◽  
Boundary Case ◽  
Active Shape ◽  
The Face ◽  
Testing Set

scholarly journals Neutral expression synthesis using kernel active shape model

2020 ◽  
Vol 20 (1) ◽  
pp. 150
Author(s):  
Marcella Peter ◽  
Jacey-Lynn Minoi ◽  
Suriani Ab Rahman
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Recognition Rate ◽  
Active Shape Model ◽  
Shape Model ◽  
Facial Identity ◽  
Modified Method ◽  
Active Shape ◽  
The Face ◽  
Recognition Systems

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.

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
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolution Neural Network ◽  
Contour Method ◽  
Connected Components ◽  
Active Shape Model ◽  
Human Face ◽  
Shape Model ◽  
Active Shape ◽  
The Face

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.

scholarly journals Biometrics Passport Authentication Using Facial Marks

2019 ◽  
Author(s):  
Ziaul Haque Choudhury
Keyword(s):  
Face Recognition ◽  
Recognition Accuracy ◽  
Personal Identification ◽  
Appearance Model ◽  
Active Shape Model ◽  
Face Database ◽  
Recognition Method ◽  
Shape Model ◽  
The Face ◽  
Canny Edge

A secure biometric passport in the field of personal identification for national security is proposed in this paper. This paper discusses about how to secure biometric passport by applying face recognition. Proper biometric features are unique for each individual and it is invariably in time, it is an unambiguous identifier of a person. But it may fail to authorize a person, if there are some changes in an applicant‘s appearance, such as a mustache, hair cut, and glasses, etc., the case of similar individuals like twins, siblings, similar faces or even doubles could head to individuality mismatch. Our proposed face recognition method is based on facial marks present in the face image to authenticate a person. We applied facial boundary detection purpose ASM (Active Shape Model) intoAAM (Active Appearance Model) using PCA (Principle Component Analysis). Facial marks are detected by applying Canny edge detector and HOG (Histogram Oriented Gradients). Experimental results reveal that our proposed method gives 94 percentage face recognition accuracy, using Indian face database from IIT, Kanpur.

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape
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