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Deep facial recognition after medical alterations

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Abstract

Medical alterations on the facial region introduce skin consistency deviations amongst images of the same individual, thus making face recognition after medical alterations complicated than in regular circumstances. Cosmetic surgeries lead to medical identity thefts. Thus this makes one’s security a serious concern and human identification after medical alterations a critical dare. As prevailing techniques for human identification after surgery are not favorable, so in the present picture cosmetic therapies score above facial recognition. Neural models are not used till date to recognize surgically altered faces. The proposed approach uses a Deep Feed Forward Neural Network to recognize surgically altered faces. The innovation lies in weight update during back propagation which results into optimization of computational complexity and thus less training. While calculating error gradient during weight update, trace of inversed Hessian matrix is evaluated instead of computing the entire matrix which results into cumbersome calculations. Trace reveals vital facial features essential for recognition. Training deep models is computationally expensive but our scheme reduces computation complexity. Rank 1 Recognition Rates (RR) are obtained empirically by bootstrap sampling with 95% confidence interval level for the plastic surgery facial dataset. RR values 97.89% and 98.24% obtained for global and local surgeries are best reported till date in literature. This dataset is unbalanced so with biased metrics (RR and MSE (Mean Square Error)) unbiased metrics (F-score and R (Regression Coefficient)) are also analyzed. The recognition results obtained are equivalent to existing deep models which are computationally expensive and require large processing power.

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Authors and Affiliations

  1. USICT, GGSIPU, Delhi, India

    Tanupreet Sabharwal

  2. Department of Electronics and Communication Engineering, NSUT East campus (Formerly AIACT&R), Delhi, India

    Rashmi Gupta

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  1. Tanupreet Sabharwal
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  2. Rashmi Gupta
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Correspondence to Rashmi Gupta.

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Sabharwal, T., Gupta, R. Deep facial recognition after medical alterations. Multimed Tools Appl 81, 25675–25706 (2022). https://doi.org/10.1007/s11042-022-12895-z

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