Automatic Prediction of Age-group from Frontal Facial Images
B. Abirami1, T. S. Subashini2
1B. Abirami, Research Scholar, Department of Computer Science and Engineering, Annamalai University, Chennai, Tamilnadu, India.
2Dr. T. S. Subashini, Associate Professor, Department of Computer Science and Engineering, Annamalai University, Chennai, Tamilnadu, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 5356-5359 | Volume-9 Issue-1, October 2019 | Retrieval Number: A3067109119/2019©BEIESP | DOI: 10.35940/ijeat.A3067.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Methods to automatically assess the age group of a person using his/her frontal facial image are proposed in this paper. This work is done for three major ethnicities: African, American and Asian with five different age-groups such as (1-10 years), (11-30 years), (31-50years), (51-70 years), (71-100 years). The performances of the classifiers were tested with face images of African, American and Asian population belonging to both genders. For this, first the facial parts such as the left eye, right eye, nose, mouth etc., are detected using the well-known Viola Jones Object Detection technique.450 sample images of the FERET database were considered for this study. Histogram of Gradient (Ho G) and face-structure features are extracted and modeled using ANN and SVM. The efficiency of the proposed methods was tested with the facial images of various races belonging to different age-group and gender. Artificial neural network gave an accuracy of 92.10% whereas support vector machine gave an improved accuracy of 94.60%.
Keywords: Face detection, face-structure features, HoG features, ANN, SVM.