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Residual and Ensemble Learning on Locally Derived Features for Face Recognition

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Intelligent Systems, Technologies and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1148))

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Abstract

In this paper, an efficient approach toward face recognition is presented. The proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. The images are first preprocessed using different techniques like CS-LBP, XCS-LBP, CS-LDMP, etc. to remove noise and make it illumination invariant. In one implementation, these are fed as input to different descriptors. The keypoints detected are invariant to lighting condition. A wide variety of descriptors are applied to the detected keypoints in order to compare the quality of features computed. These descriptors are quantized to a single vector, representing the most salient features of the facial image. The vectors are then fed into the stacking classifier. In the other method, these preprocessed images are directly fed as input into the residual network. A comparison of the results by these two pipelines is studied on two benchmark face datasets, namely FACES94 and Grimace.

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Author information

Authors and Affiliations

  1. Centre for Pattern Recognition and Machine Intelligence, PES University, Bengaluru, KA, 560085, India

    A. Vinay, Abhijay Gupta, Aprameya Bharadwaj, Arvind Srinivasan, K. N. Balasubramanya Murthy & S. Natarajan

Authors
  1. A. Vinay
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  2. Abhijay Gupta
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  3. Aprameya Bharadwaj
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  4. Arvind Srinivasan
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  5. K. N. Balasubramanya Murthy
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  6. S. Natarajan
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Corresponding author

Correspondence to Aprameya Bharadwaj .

Editor information

Editors and Affiliations

  1. School of CS/IT, Indian Institute of Information Technology and Management-Kerala (IIITM-K), Technopark Campus, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

    Ljiljana Trajkovic

  3. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  4. Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India

    P. Nagabhushan

  5. College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  6. Engineering Academy of Serbia, University of Belgrade, Belgrade, Serbia

    Zoran Bojkovic

  7. Indian Institute of Space Science and Technology, Trivandrum, Kerala, India

    Deepak Mishra

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Vinay, A., Gupta, A., Bharadwaj, A., Srinivasan, A., Balasubramanya Murthy, K.N., Natarajan, S. (2020). Residual and Ensemble Learning on Locally Derived Features for Face Recognition. In: Thampi, S., et al. Intelligent Systems, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-3914-5_2

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