Transcript Slide 1
Face Recognition Based Dog Breed Classification Using Coarse-to-Fine Concept and PCA Massinee Chanvichitkul, Pinit Kumhom, Kosin Chamnongthai King Mongkut’s University of Technology Thonburi
(KMUTT)
Presentation Outline
Abstract Motivation Problem Analysis and Basic Concept System and Method Experiments and Results Disscussion Conclusion
Abstract
There are 154 dog breeds , and some are similar in face configurations We propose a modified PCA-based method to classify dog-face images. Classification tolerances among dog-face images are widened by coarse-to-fine concept . In coarse classification, 12 patterns of dog-face profile are employed to group dog faces. PCA is then used as a tool for fine classification in each group The experiments show that the accuracy of the proposed system is better than the PCA-based classifier. The improvement is around 20% approximately.
Motivation
Dogs have been the most favorite and popular pet of humans According to American Kennel Club (AKC), there are more than 154 dog breeds and some are similar in face configurations e.g. Labrador, Golden, German shepherd, and so on Dogs need a specific treat due to their breed , we have to recognize dog breeds in order to appropriately care and cure .
Thus an automatic dog breed classifier can be used as a standalone classification tool or an assistant to the experts in giving essential information for speeding up the classification process
Problem Analysis and Basic Concept-I
Unlike human face classification, a variety of dog faces could be grouped by using ear profile and face profile patterns.
This paper would refer ear and face profile patterns as a global feature.
Categorized by ears, dog breed can be divided into two groups, i.e. Standing Ear and Dropped Ear From face profile, dog breed can be divided into six patterns, i.e. Square, Ellipse, Trapezoid, Circle, Traingle and Hexagon patterns.
Totally, there are twelve groups of ear and face profiles.
Problem Analysis and Basic Concept-II
In Coarse classification: grouping dog breeds into 12 groups using ear and face profiles.
Ear profile Face profile
Problem Analysis and Basic Concept-III
In Fig. 1, the Fig. shows that we can classify two of dog faces (Miniture and Dollberman) by ear profile feature (standing ear or dropped ear).
Miniture Dollberman
Figure 1 Example of dog images are in the different group
Problem Analysis and Basic Concept-IV
In Fig. 2, the Fig. shows that we can classify two of dog faces (Englishfog and Harley) by face profile feature (Triangle-like or Circle-like face) Englishfog Harley
Figure 2 Example of dog images are in the same group
Problem Analysis and Basic Concept-V
Table I three examples of grouping dogs by face and ear profiles
SE-TR DE-CI DE-TR
From the basic concept, we would be able to construct the proposed system for classifying dog face images as shown in Fig. 3.
Proposed system-I
A dog face image Contour-based classifier PCA-based classifier Output
Figure 3 the proposed system
Proposed system-II
Figure 4: Contour-based Coarse classification Figure 5: PCA-based Fine classification
Proposed system-III
Step 1
: Input a dog face image.
Step 2
: Finding the image contours.
Step 3
: Classifying the input image using the Fourier descriptor.
Step 4
: Deciding the group of a dog face image.
Step 5
: Classifying a dog face image using the PCA-based classifier.
Experiments &Results-I
The example of coarse classification of the SE-SQ template: Im(i) Curvature x Im(i) Curvature y 150 100 50 150 100 50 0 0 1500 0 0 500 y(k) 1000 1500 500 x(k) 1000 Figure 6: (a) the image curvature x and y
100
Experiments &Results-II
Harmonic Components of the Curvature x 50 0 0 2 4 6 8 10 12 Harmonic order 14 Harmonic Component of the Curvature y 16 18 20 100 50 0 0 2 4 6 8 10 12 Harmonic order 14 16 18 Figure 6: (b) the frequency spectrum of the curvature x and y 20
Experiments &Results-III
150 100 50 0 0 Im(i) Curvature x 500 x(k) 1000 100 80 60 40 20 0 Im(i) Curvature y 500 y(k) 1000
Figure 7 (a) The coarse classification of the doberman face: the image contours x(k) and y(k)
Im(i) Curvature x Im(i) Curvature y 150 100 50 0 0 150 100 1000 50 0 0 500 x(k) 500 y(k) 1000
Figure 8 (a) The coarse classification of the english toy terrier face: the image contours x(k) and y(k)
Experiments &Results-IV
Harmonic Components of the Curvature x Harmonic Components of the Curvature x 100 100 50 50 0 0 2 100 0 4 6 8 10 12 Harmonic order 14 Harmonic Component of the Curvature y 16 18 20 100 0 2 4 6 8 10 12 Harmonic order 14 Harmonic Component of the Curvature y 16 18 20 50 50 0 0 2 4 6 8 10 12 Harmonic order 14 16 18
Figure 7 (b) The coarse classification of the doberman face: the frequency spectrum of the image.
20 0 0 2 4 6 8 10 12 Harmonic order 14 16 18 20
Figure 8 (b)The coarse classification of the English Toy Terrier face: the frequency spectrum of the image.
Experiments &Results-V
Table 2. shows the results of the proposed method comparing with a PCA-based classifier shows the classification results of the proposed classifier comparing with the PCA-based classifier.
The classification improvement is around 20% approximately.
Breeds
Minature paniture Minature terrier Beagle Golden retriever Labrador English toy terrier Chihuahua Doberman Shiba inu Siberian husky
Sample
8 8 8 8 8 7 7 7 6 6
PCA (%)
66.7
66.7
75 66.7
66.7
71.4
66.7
66.7
66.7
75
Proposed (%) Improvement (%)
87.5
20.8
87.5
87.5
87.5
75 20.8
12.5
20.8
8.3
85.7
85.7
85.7
83.3
83.3
14.29
19.01
19.01
16.63
8.33
Conclusions
This paper presents the classification of dog breed images using the coarse-to-fine approach.
In order to achieve the coarse-to-fine classification concept, the contour-based classifier and the PCA-based classifier are used as the coarse classifier and the fine classifier respectively In the experiments, 73 of dog face images are tested with the proposed classification system comparing with the PCA based classification system The experiments show that the accuracy of the proposed system is better than the PCA-based classifier . From Table2, the improvement is approximately.
around 20%
Discussion
Bulldog Bulldog
When we classify using coarse classification two images are in different groups, thus the accuracy of the system would be decrease.