International Conference on Innovations in Science, Engineering and Technology 2016 (ICISET 2016)

Permanent URI for this collectionhttp://dspace.iiuc.ac.bd/handle/88203/338

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Subject: search.filters.subject.Kernel PCA

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    Hand Sign Language Recognition for Bangla Alphabet using Support Vector Machine
    (IEEE, 2016-10-28) Uddin, Md Azher; Chowdhury, Shayhan Ameen
    The sign language considered as the main language for deaf and dumb people. So, a translator is needed when a normal person wants to talk with a deaf or dumb person. In this paper, we present a framework for recognizing Bangla Sign Language (BSL) using Support Vector Machine. The Bangla hand sign alphabets for both vowels and consonants have been used to train and test the recognition system. Bangla sign alphabets are recognized by analyzing its shape and comparing its features that differentiates each sign. In proposed system, hand signs are first converted to HSV color space from RGB image. Then Gabor filters are used to acquire desired hand sign features. Since feature vector obtained using Gabor filter is in a high dimension, to reduce the dimensionality a nonlinear dimensionality reduction technique that is Kernel PCA has been used. Lastly, Support Vector Machine (SVM) is employed for classification of candidate features. The experimental results show that our proposed method outperforms the existing work on Bengali hand sign recognition.
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    An Integrated Approach to Classify Gender and Ethnicity
    (IEEE, 2016-10-28) Uddin, Md Azher; Chowdhury, Shayhan Ameen
    Faces express many social indications, including gender, ethnicity, age, expression and identity, most of them have drawn thriving attention from various research communities, for instance neuroscience, computer science and psychology. In this paper, we propose a new approach to classify gender and ethnicity by merging both texture and shape features extracted from face images. Gabor filter is used to extract the texture features and histogram of oriented gradients (HOG) is used to extract the shape features from face images. In order to achieve higher performance we combined both texture and shape features. After combining, the size of feature vector obtained is in a high dimension, to decrease the dimensionality Kernel PCA has been implemented. Finally, to classify the gender and ethnicity we used Support Vector Machine. The experimental result shows the effectiveness of proposed framework.