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

Ear profile

Face profile

In Coarse classification: grouping dog breeds into 12 groups using ear and face profiles.

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)

HarleyEnglishfog

Figure 2 Example of dog images are in the same group

Problem Analysis and Basic Concept-V

SE-TR

DE-CI

DE-TR

Table I three examples of grouping dogs by face and ear profiles

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

Contour-basedclassifier PCA-based

classifier Output

A dog faceimage

Figure 3 the proposed system

Proposed system-II

Figure 5: PCA-based Fine classification

Figure 4: Contour-based Coarse 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.

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The example of coarse classification of the SE-SQ template:

Experiments &Results-I

Figure 6: (a) the image curvature x and y

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Experiments &Results-II

Figure 6: (b) the frequency spectrum of the curvature x and y

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Figure 7 (a) The coarse classification of the doberman face: the image contours x(k) and y(k)

Figure 8 (a) The coarse classification of the english toy terrier face: the image contours x(k) and y(k)

Experiments &Results-III

Experiments &Results-IV

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Figure 7 (b) The coarse classification of the doberman face: the frequency spectrum of the image.

Figure 8 (b)The coarse classification of the English Toy Terrier face: the frequency spectrum of the image.

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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.

Experiments &Results-V

Breeds Sample PCA (%) Proposed (%)

Improvement (%)

Minature paniture 8 66.7 87.5 20.8

Minature terrier 8 66.7 87.5 20.8

Beagle 8 75 87.5 12.5

Golden retriever 8 66.7 87.5 20.8

Labrador 8 66.7 75 8.3

English toy terrier 7 71.4 85.7 14.29

Chihuahua 7 66.7 85.7 19.01

Doberman 7 66.7 85.7 19.01

Shiba inu 6 66.7 83.3 16.63

Siberian husky 6 75 83.3 8.33

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 around 20%

approximately.

Conclusions

Discussion

When we classify using coarse classification two images are in different groups, thus the accuracy of the system would be decrease.

BulldogBulldog

Thank you for YourAttention !

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