Facial Expression Recognition using LBP, DWT and SVM Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.6 · Issue 11

Published

June 2018

Access

Open Access

Licence

CC BY-NC-SA 4.0

Kavita Suthar Komal Paliwal

The face though seems an easy object to be recognized by retina but the artificial intelligence is not yet intelligent enough to do the task easily. As the source of a face is generally an image capturing object, there are a lot of variations and complexions that persists with the image like (for example noise, rotation etc.). There are many techniques that use some algorithm to find similarity in the face model and the test image and most of them are successful on their part to attain better test similarities. However, considering the diverse scale of applications and mode of image sourcing, a single algorithm cannot get maximum efficiency everywhere. Even after using the best algorithm for a particular task, an application has to counter with challenges of face recognition.
This paper analyzes the hybrid approach of Local Binary Pattern (LBP) and DWT (Discrete Wavelet Transform) features for facial expression recognition. A subspace is created by this algorithm for training of feature vectors and Support Vector Machine (SVM) Classifier calculates the similarity score for performance evaluation which will provide improved results in terms of recognition accuracy.

Kavita Suthar, Komal Paliwal (2018). Facial Expression Recognition using LBP, DWT and SVM Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.6, Issue 11. ISSN: 2319-4863.

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