Smile Detection by Boosting Pixel Differences

NANO SCIENTIFIC RESEARCH CENTRE PVT.LTD.,  AMEERPET, HYD

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DOT NET PROJECTS LIST--2013

DOT NET 2013 IEEE PAPERS

Smile Detection by Boosting Pixel Differences

Abstract:

            Smile detection in face images captured in unconstrained real-world scenarios is an interesting problem with many potential applications. This paper presents an efficient approach to smile detection, in which the intensity differences between pixels in the grayscale face images are used as features. We adopt AdaBoost to choose and combine weak classifiers

based on intensity differences to form a strong classifier. Experiments show that our approach has similar accuracy to the state-of-the-art method but is significantly faster. Our approach provides 85% accuracy by examining 20 pairs of pixels and 88% accuracy with 100 pairs of pixels. We match the accuracy of the Gabor-feature-based support vector machine using as few as 350 pairs of pixels.

Existing System:

            Although there is a large amount of literature on facial expression recognition, few papers have focused specifically on smile detection. Some of Authors like Shinohara and Otsu integrated higher order local autocorrelation features with a Fisher weight map for face representation. With the Fisher discriminant analysis, their approach provides the accuracy of 97.9% on 96 face images. Although high accuracy was achieved, very limited data were used in their study.

Proposed System:

            In this paper, we focus on smile detection in face images captured in real-world scenarios. Fig. 1 shows some example images. We present an efficient approach to smile detection, in which the intensity differences between pixels in the grayscale face images are used as simple features. AdaBoost then is adopted to choose and combine weak classifiers based on pixel differences to form a strong classifier for smile detection.

Software and Hardware Requirements

Hardware Required:                            

System                                    :           Pentium IV

Hard Disk                   :           80 GB

RAM                           :           512 MB

Software Required:

Operating System       :           Windows XP

Language                    :           C#

Modules:

·         Access Image

·         Convert Image into Grey Image

·         Detect Face, Mouth Areas

·         Detect Smile