Computational Intelligence
Kaushal Kishore Rao Mangalore; Nikhitha Pradeep; Bhawesh Rajpal; Nitin Prasad; Ravi Shastri
Abstract
The move to standardize Indian Sign Language has created an opportunity for researchers to focus on solving local problems, to increase its reach. In this paper, a survey and assessment of the techniques applied to the recognition and conversion of Indian Sign Language are performed. An overview of the ...
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The move to standardize Indian Sign Language has created an opportunity for researchers to focus on solving local problems, to increase its reach. In this paper, a survey and assessment of the techniques applied to the recognition and conversion of Indian Sign Language are performed. An overview of the techniques used in sign language recognition for Indian Sign Language is provided to understand the status of research in this field. Following this, a comparison of techniques aimed at rendering a more detailed picture of the research results is presented. The challenges faced by researchers, the limitations of current techniques, and the need for improved research in this area are highlighted. With the intent of spurring more in-depth research, key areas within the approaches and techniques in need of improvement are summarized.
Computational Intelligence
Bhawesh Rajpal; Nitin Prasad; Kaushal Kishore Rao Mangalore; Nikhitha Pradeep; Ravi Shastri
Abstract
This paper consists of analysis of an algorithm dealing with facial expressions recognition. The algorithm has three major steps, initially image is processed, then the facial features are extracted and finally facial expression is recognized. In the initial processing stage the facial region is identified ...
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This paper consists of analysis of an algorithm dealing with facial expressions recognition. The algorithm has three major steps, initially image is processed, then the facial features are extracted and finally facial expression is recognized. In the initial processing stage the facial region is identified using a Haar cascade classifier. This facial region is passed on to the model trained by a CNN where facial features are matched with the features specified in the model. In the final step on the basis of comparison in the previous step the image is labelled and results are displayed. By the experiment results it is clear that the method specified in the paper can detect facial expressions very well.