Age Classification Based On Appearance Model Using Local Ternary Direction Pattern Approach
Nara Sreekanth1, Munaga HM Krishna Prasad2
1Nara Sreekanth, Research Scholar, Department of Computer Science and Engineering, BVRIT Hyderabad College of Engineering for Women, Bachupally, Telengana, India.
2Munaga HM Krishna Prasad, Professor, Department of Computer Science and Engineering, University College of Engineering Kakinada, JNTUK, Pithapuram Road, Nagamallithota, Kakinada, Andhra Pradesh, India.
Manuscript received on 10 December 2018 | Revised Manuscript received on 17 December 2018 | Manuscript Published on 30 December 2018 | PP: 446-454 | Volume-8 Issue- 2S December 2018 | Retrieval Number: BS2722128218/19©BEIESP
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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: The appearance model play a vital role in many applications related to facial images. This paper derives a new approach of appearance model using local ternary derivative patterns on human facial images for effective age groups classification. In the literature direction patterns are derived with respect to central pixel of the neighborhood. This paper derives ternary direction patters (TDP) between sampling points of the neighborhood with a strong assumption that the relationship between adjacent pixels derive rich information. This paper divides the neighborhood into vertical and horizontal units and derives the TDP and based on the relative frequencies of horizontal and vertical TDP, this paper derives horizontal vertical direction unit matrix (HVDUM). The gray level co-occurrence matrix (GLCM) features are derived on HVDUM for age classification and the experimental results are compared with the existing methods and the results indicate the efficiency of the proposed method over the existing methods.
Keywords: Neighborhood; Vertical-Horizontal Units; GLCM Features; Sampling Points
Scope of the Article: Classification