Face Recognition Based Dog Breed Classification Using Coarse to-Fine Concept and PCA

Name: Massinee Chanvichitkul

Organization : King Mongkut's University of Technology Thonburi. Faculty of Engineering. Electrical and Information Engineering

keyword: Face Recognition

; Pattern Recognition

Abstract: Dog breed classification or identification is important for dog training, and curing. The conventional identification method is based on experts which are hard to find. This research study proposes a method to classify dog breed based on the dog face images. The proposed method is based on the coarse to fine concept, where the template matching technique is applied for coarsely classifying the images into 5 groups. Then, within each group, the Principle Component Analysis (PCA) is applied to finely classifying the dog breed. In the PCA-based classification, face features are represented in term of a weight vector. A set of sample images of each dog breed is used for learning the features of the breed. The average weight vectors are stored as the templates of features for breeds after the coarse classification. During the running time after the coarse classification, a dog face image is passed through the PCA to find it vector representation. This vector will then be compared with feature template for each breed in the database. The image under test is classified as the breed that gives the minimum distance between the two vectors. To evaluate the performance of the proposed method, experiments with 700 dog face images from 35 dog breeds had been performed. Before the testing, five dog face images from every breed (totally 175 dog faces) were used to train the system. The experiments showed that the overall proposed method gives approximately 92 % accuracy, which is better than the PCA-based classifier without the coarse classification. The improvement is 16% approximately.

King Mongkut's University of Technology Thonburi

Address: Bangkok

Email: info.lib@mail.kmutt.ac.th

Name: Kosin Chamnongthai

Role: Advisor

Name: Pinit Kumhom

Role: Advisor

Created: 2006

Modified: 2010-12-19

Issued: 2009-02-13

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: ENE651

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Electrical and Information Engineering

Grantor: King Mongkut's University of Technology Thonburi

©copyrights King Mongkut's University of Technology Thonburi

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