Work place: Maharaja Agrasen College, University of Delhi, Vasundhara Enclave, Delhi - 110096, India
Research Interests: Pattern Recognition, Natural Language Processing
Meetika Sharma was born in Delhi, India, in 1994. She received the B.Tech degree in computer science from the University of Delhi, Delhi, India, in 2017. She is currently doing masters in computer science with specialization in ‘Intelligent Systems’ from The University of Texas at Dallas, Dallas, USA. She has published a few research papers in the field of machine learning. Her current research interests include machine learning, pattern matching and natural processing language. She was the recipient of ‘Indira Award’ from Directorate of Education, Delhi, India
DOI: https://doi.org/10.5815/ijem.2018.01.06, Pub. Date: 8 Jan. 2018
Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural Networks (CovNets), a type of deep networks has been proved to be successful for FR. For real-time systems, some preprocessing steps like sampling needs to be done before using to CovNets. But then also complete images (all the pixel values) are passed as input to CovNets and all the steps (feature selection, feature extraction, training) are performed by the network. This is the reason that implementing CovNets are sometimes complex and time consuming. CovNets are at the nascent stage and the accuracies obtained are very high, so they have a long way to go. The paper proposes a new way of using a deep neural network (another type of deep network) for face recognition. In this approach, instead of providing raw pixel values as input, only the extracted facial features are provided. This lowers the complexity of while providing the accuracy of 97.05% on Yale faces dataset.[...] Read more.
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