Face and Facial Expression Recognition - Fusion based Non Negative Matrix Factorization

Humayra Ali, David Powers

    Research output: Contribution to conferencePaper

    1 Citation (Scopus)

    Abstract

    Face and facial expression recognition is a broad research domain in machine learning domain. Non-negative matrix factorization (NMF) is a very recent technique for data decomposition and image analysis. Here we propose face identification system as well as a facial expression recognition, which is a system based on NMF. We get a significant result for face recognition. We test on CK+ and JAFFE dataset and we find the face identification accuracy is nearly 99% and 96.5% respectively. But the facial expression recognition (FER) rate is not as good as it required for the real life implementation. To increase the detection rate for facial expression recognition, our propose fusion based NMF, named as OEPA-NMF, where OEPA means Optimal Expressionspecific Parts Accumulation. Our experimental result shows OEPA-NMF outperforms the prevalence NMF for facial expression recognition. As face identification using NMF has a good accuracy rate, so we are not interested to apply OEPA-NMF for face identification.

    Original languageEnglish
    Pages426-434
    Number of pages9
    Publication statusPublished - 1 Jan 2015
    EventInternational Conference on Agents and Artificial Intelligence (ICAART-2015) -
    Duration: 10 Jan 2015 → …

    Conference

    ConferenceInternational Conference on Agents and Artificial Intelligence (ICAART-2015)
    Period10/01/15 → …

    Keywords

    • Fer-facial expression recognition
    • FR-face recognition
    • NMF-non negative matrix factorization
    • OEPA-optimal expression-specific parts accumulation

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  • Cite this

    Ali, H., & Powers, D. (2015). Face and Facial Expression Recognition - Fusion based Non Negative Matrix Factorization. 426-434. Paper presented at International Conference on Agents and Artificial Intelligence (ICAART-2015), .