Deepjoy Das

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The study evaluates three background subtraction techniques. The techniques ranges from very basic algorithm to state of the art published techniques categorized based on speed, memory requirements and accuracy. Such a review can effectively guide the designer to select the most suitable method for a given application in a principled way. The algorithms(More)
  • Deepjoy Das
  • 2015 International Conference on Electronic…
  • 2015
The paper describes the work on human gait recognition using Hidden Markov Model (HMM), Support Vector Machine (SVM) and Hybridized classifiers (developed using both HMM and SVM). Human gait data obtained from CASIA gait database were segmented to locate major human body part and generate corresponding stick view in order to extract gait features. A total(More)
The paper presents an approach towards human gender recognition system. The Silhouettes from Center for Biometrics and Security Research (CASIA) gait database are segmented in order to identify major body points and to generate corresponding point-light display. The features such as two dimensional coordinates of major body points and joint angles are(More)
This work emphasises on the development of Assamese online character recognition system using HMM and SVM and performs a recognition performance analysis for both models. Recognition models using HTK (HMM Toolkit) and LIBSVM (SVM Toolkit) are generated by training 181 different Assamese Stokes. Stroke and Akshara level testing are performed separately. In(More)
The work describes the development of Online Assamese Stroke & Akshara Recognizer based on a set of language rules. In handwriting literature strokes are composed of two coordinate trace in between pen down and pen up labels. The Assamese aksharas are combination of a number of strokes, the maximum number of strokes taken to make a combination being eight.(More)
The work presents an approach towards facial emotion recognition using face dataset consisting of four classes of emotions (happy, angry, neutral and sad) with different models of deep neural networks and compares their performance. We take the raw pixels values of all images in CMU face images dataset. The pixels values were represented by higher level(More)
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