A Study of Identification Performance of Facial Regions from CCTV Images

This paper focuses on automatic face identification for forensic applications. Forensic examiners compare different parts of the face image obtained from a closed-circuit television (CCTV) image with a database of mug shots or good quality image(s) taken from the suspect. In this work we study and compare the discriminative capabilities of different facial regions (also referred to as facial features) such as eye, eyebrow, mouth, etc. It is useful because it can statistically support the current practice of forensic facial comparison. It is also of interest to biometrics as a more robust general-purpose face recognition system can be built by fusing the similarity scores obtained from the comparison of different individual parts of the face. For experiments with automatic systems, we simulate a very challenging recognition scenario by using a database of 130 subjects each having only one gallery image. Gallery images are frontal mug shots while probe set consist of low quality CCTV camera images. Face images in gallery and probe sets are first segmented using eye locations and recognition experiments are performed for the different face regions considered. We also study and evaluate an improved recognition approach based on AdaBoost algorithm with Linear Discriminant Analysis (LDA) as a week learner and compare its performance with the baseline Eigenface method for automatic facial feature recognition.