Enhanced age prediction and gender classification (EAP-GC) framework using regression and SVM techniques

2020 ◽

Vol 9 (6) ◽

pp. 797-801

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Real Time ◽

Vital Role ◽

Learning Techniques ◽

Gender And Age ◽

The Face ◽

And Gender ◽

Gender Detection ◽

Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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Human Age And Gender Classification using Convolutional Neural Network

2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) ◽

10.1109/ihsh51661.2021.9378708 ◽

2021 ◽

Author(s):

Mohammed Kamel Benkaddour ◽

Sara Lahlali ◽

Maroua Trabelsi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gender Classification ◽

Age And Gender ◽

And Gender

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Invariant Model Combining Geometry and Appearance for Facial Detection and Gender Classification From Arbitrary Viewpoints

Advances in Multimedia and Interactive Technologies - Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing ◽

10.4018/978-1-5225-2848-7.ch009 ◽

2017 ◽

pp. 196-231

Author(s):

Mokhtar Taffar ◽

Serge Miguet

Keyword(s):

Face Detection ◽

Probabilistic Model ◽

Facial Feature ◽

Facial Appearance ◽

Gender Classification ◽

Invariant Model ◽

Model Combining ◽

Probabilistic Classification ◽

Wide Range ◽

And Gender

In this chapter, we tackle in the same process the problems of face detection and gender classification, where the faces present a wide range of the intra-class appearance are taken from arbitrary viewpoints. We try to develop complete probabilistic model to represent and learn appearance of facial objects in both shape and geometry with respect to a landmark in the image, and then to be able to predict presence and position of the appearance of the studied object class in new scene. After have predicted the facial appearance and the geometry of invariants, geometric hierarchical clustering combines different prediction of positions of face invariant. Then, the algorithm of cluster selection with a best appearance localizes faces in the image. Using a probabilistic classification, each facial feature retained in the detection step will be weighted by a probability to be male or female. This set of features contributes to determine the gender associated to a detected face. This model has a good performance in presence of viewpoint changes and a large appearance variability of faces.

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