Recognition of surgically altered face images: an empirical analysis on recent advances

Biometric recognition plays a vital role in our daily lives. Face recognition is a subset of biometric recognition. Face verification and identification processes are prone to plastic surgery challenges which are commonly used nowadays to alter facial features for good looking demonstration. With increasing trend in technology and intellect robust biometric recognition systems are developed for human recognition after plastic surgery. However, these systems have some limitations because recognition after plastic surgery is affected by lightning, aging, pose, expressions, disguise and occlusion effects. In this survey, we aim to highlight the mitigating effects of cutting edge plastic surgical operations. These procedures lead to medical identity thefts, which is a serious offense for human community as an individual’s identity is forged. Thus, this makes one’s safety a critical issue and human recognition after plastic surgery a crucial challenge. Since the existing methods for human recognition after plastic surgical operations are not promising, in the current scenario plastic surgical operations secure above facial recognition. A number of existing biometric recognition algorithms for face images have been opted such as principal component analysis, Fisher/linear discriminant analysis, local feature analysis, local/circular binary patterns, speeded up robust features, granular system, correlation based approach, evolutionary granular/genetic approach, grouping recognition by parts and sparse demonstration approach, geometrical face recognition after plastic surgery, feature/texture based fusion scheme and deep convolutional neural networks (DCNN). The validation metrics used for the evaluation of recognition techniques are expected error rate, recognition rate, half total error rate and F-score. All algorithms are tested on an open plastic surgery facial dataset containing 1800 before and after surgery image samples pertaining to 900 humans. For a particular human being, two front facing image samples with appropriate luminance and unbiased gesture are taken: the former is taken pre cosmetic procedure and the latter is taken post cosmetic procedure. It has been deduced that feature and texture based fusion approach gives best results till date. It is predicted that DCNN has full potential of giving consistent results on surgical databases as it is already validated on non surgical databases. The need of a novel human identification system which is steady to the anomalies posed by plastic surgical operations is highlighted in this survey.