N. Palanivel

About

N. Palanivel is a researcher whose work lies at the intersection of computer vision, pattern recognition, and human-robot interaction. His most cited contribution, "Pose invariant face recognition using HMM and SVM using PCA for dimensionality reduction" (2014), presents an embedded system that integrates Hidden Markov Models (HMM) with Support Vector Machines (SVM) to achieve robust, pose-invariant face recognition. By employing Principal Component Analysis (PCA) for dimensionality reduction, the system efficiently handles full-pose variations from face databases, making it particularly suitable for real-time applications in Human-Robot Interaction. This work, with 3 citations, demonstrates a practical approach to overcoming one of the key challenges in automated facial recognition: maintaining accuracy across different head orientations. Palanivel’s research contributes to the development of more reliable and responsive robotic systems capable of recognizing human faces in natural, unconstrained environments. His focus on combining statistical models with machine learning classifiers highlights a commitment to creating computationally efficient solutions for interactive technologies.

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Top Papers

1. 1
   Pose invariant face recognition using HMM and SVM using PCA for dimensionality reduction

   3 citations · 2014

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