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Human Facial Expression Detection From Detected in Captured Image using Back Propagation Neural Network


Affiliations
1 Digital Systems Group, Central Electronics Engineering Research Institute (CEERI)/Council of Scientific and Industrial Research (CSIR), Pilani-333031, Rajasthan, India
 

In the field of image processing it is very interesting to recognize the human gesture for general life applications. For example, observing the gesture of a driver when he/she is driving and alerting him/her when in sleepy mood will be quite useful. Human gestures can be identified by observing the different movements of eyes, mouth, nose and hands. In this paper we are focusing on the human face for recognizing expression. Many techniques are available to recognize face. In this paper, face is detected using the Viola and Jones techniques. This paper introduces a simple architecture for human facial expression recognition. The approach is based on add-boosted classifier for face detection and simple token finding and matching using back propagation neural network. This approach can be adapted to real time system very easily. The paper briefly describes the schemes of capturing the image from web cam, detecting the face, processing the image to recognize the gestures as well as few results.

Keywords

Add Boosted Classifier, Edge Detection, Face Detection, Gesture Recognition, Neural Network, Token Detection.
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  • Human Facial Expression Detection From Detected in Captured Image using Back Propagation Neural Network

Abstract Views: 350  |  PDF Views: 185

Authors

Jagdish Lal Raheja
Digital Systems Group, Central Electronics Engineering Research Institute (CEERI)/Council of Scientific and Industrial Research (CSIR), Pilani-333031, Rajasthan, India
Umesh Kumar
Digital Systems Group, Central Electronics Engineering Research Institute (CEERI)/Council of Scientific and Industrial Research (CSIR), Pilani-333031, Rajasthan, India

Abstract


In the field of image processing it is very interesting to recognize the human gesture for general life applications. For example, observing the gesture of a driver when he/she is driving and alerting him/her when in sleepy mood will be quite useful. Human gestures can be identified by observing the different movements of eyes, mouth, nose and hands. In this paper we are focusing on the human face for recognizing expression. Many techniques are available to recognize face. In this paper, face is detected using the Viola and Jones techniques. This paper introduces a simple architecture for human facial expression recognition. The approach is based on add-boosted classifier for face detection and simple token finding and matching using back propagation neural network. This approach can be adapted to real time system very easily. The paper briefly describes the schemes of capturing the image from web cam, detecting the face, processing the image to recognize the gestures as well as few results.

Keywords


Add Boosted Classifier, Edge Detection, Face Detection, Gesture Recognition, Neural Network, Token Detection.