Facial recognition techniques comparison for in-field applications : Database setup and environmental influence of the access control

A currently ongoing project at Stanley Security is to develop a facial recognition system as access control system to prevent theft of heavy vehicles for terrorist purposes. As of today, there are several providers of facial recognition techniques and the spectrum ranges from multi dollar licenses to easy access open source. This fact, the range of different algorithms and the difficulty to estimate their differences in performance is the fundamental inspiration to investigate in this thesis. Four facial recognition algorithms, Eigenfaces, Fisherfaces, Local Binary Patterns Histogram and Convolutional Neural Network, that are easily accessible and open source were therefore chosen and investigated. They were implemented and tested on the STANLEY database. The STANLEY database was built up for this test specifically. It contains of 11 persons with 10 pictures each, taken from the driver’s seat, including sunglasses, various head sizes and strong local face lighting and shadowing to simulate the environment in which the algorithms were tested. The Convolutional Neural Network algorithm had the highest precision, the least false positives and was also the fastest in the speed test. The Eigenfaces and Fisherfaces algorithms were not very robust and had trouble with images where high local face lighting and shadowing were present and are therefore considered not suited for access control for heavy vehicles. The Local Binary Patters Histogram algorithm stood out from the Eigenfaces and Fisherfaces algorithms but was still not near the Convolutional Neural Network algorithm in performance. The Convolutional Neural Network algorithm had a precision or true positive rate of 95,8% of the persons on all the given images and had 0% false positives. The precision for the Eigenface, Fisherface and LBPH algorithms were 16.2%, 17,6% and 26,5% respectively and the true positives were 23,5%, 24,5% and 10,2% respectively. The high false positive rate would have negative impact on access control applications. The convolutional neural network-based algorithm was concluded to be the facial recognition technique of choice for STANLEY Security and the biggest obstacle for implementing and commercializing this solution is the jurisdictional aspects regarding license and usage of code and specifically the pre-trained face recognition model. The jurisdictional aspect was never treated in this thesis although it was one of the extensions