A robust technique for real-time face verification with a generative network

İbrahim Batuhan Akkaya; Kaan Karaman

doi:10.1117/12.2558526

11 May 2020 A robust technique for real-time face verification with a generative network

İbrahim Batuhan Akkaya, Kaan Karaman

Proceedings Volume 11401, Real-Time Image Processing and Deep Learning 2020; 1140107 (2020) https://doi.org/10.1117/12.2558526
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Real-time face verification from a live stream is still an open question, although it is quite popular in recent years. In order to overcome this problem, several bio-metric techniques are widely used for authentication purposes, both in military and civilian areas. It is merely the task of detecting and comparing a candidate face with the other faces in the database to validate whether they are the same person or not. Typically, A face verification pipeline is composed of four stages: Face detection, alignment, recognition, and matching. The faces in the frames of the live stream are detected via a deep neural network (DNN) in the first part. Then, the detected faces are aligned, and another DNN extracts the face features. The feature vector of each face is used for matching with other vectors in the database to validate the identity. New developments in deep learning lead to achieving human-level performance on the aforementioned tasks. However, the networks used in the stages require high computation power. In order to achieve real-time performance in resource-limited devices, lightweight networks should be preferred. Unfortunately, usage of these kinds of networks decreases the detection and recognition performance dramatically in some frames of a live stream. Therefore, the set of feature vectors for an individual, collected from the live stream, contains outliers that complicate obtaining a robust reference feature vector, which is essential for achieving high confidence in verification tasks. In this work, a conditional generative network is utilized for generating these vectors for the given candidate. We conduct the experiments on a real-life scenario for showing the incrementation of performance that is caused by our proposed generative network.

Citation Download Citation

İbrahim Batuhan Akkaya and Kaan Karaman "A robust technique for real-time face verification with a generative network", Proc. SPIE 11401, Real-Time Image Processing and Deep Learning 2020, 1140107 (11 May 2020); https://doi.org/10.1117/12.2558526

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KEYWORDS

Feature extraction

Facial recognition systems

Neural networks

Computer programming

Principal component analysis

Video

Databases

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