Face Recognition and Plastic Surgery: Social, Ethical and Engineering Challenges (original) (raw)

IJERT-A Literature Review : Effect of Plastic Surgery on Face Recognition

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/a-literature-review-effect-of-plastic-surgery-on-face-recognition https://www.ijert.org/research/a-literature-review-effect-of-plastic-surgery-on-face-recognition-IJERTV2IS121072.pdf Variation in pose, expression, illumination, occlusion and aging are the major problem in face recognition and algorithms have been proposed to handle these challenges. Except this new problem in face recognition is plastic surgery. This problem remains still less explored topic in face recognition domain. This paper focuses on analyzing the effect of plastic surgery in face recognition algorithms. Also explain the reason for plastic surgery and various types of facial surgery due to which textural as well as shapial feature of the face will change and degrade the performances of face recognition algorithm. Therefore, it is imperative for future face recognition systems to be able to address this important issue and hence there is a need for more research in this important area.

AN INNOVATIVE APPROACH FOR PLASTIC SURGERY FACE RECOGNITION-A REVIEW

The face recognition has great significance in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. Advancement and affordability is leading to popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a great extent thereby posing a great challenge for face recognition algorithms. It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery, which thus poses a new challenge to automatic face recognition. There are several effective methods invented in recent past. So here we are suggesting an innovative approach to find out a mean method that will provide the most accurate result even after the subject has undergone a plastic surgery with higher accuracy and better response rate. This method consist of finding a mean image, which is obtained by applying several popular methods like PCA, LBP along with periocular biometrics to the test image. For comparing pre and post surgery face images Euclidean distance is used.

Plastic Surgery: An Obstacle for Deep Face Recognition?

2020

The impacts of plastic surgery on face recognition systems have been investigated in the past decade by many researchers. Diverse well-known face recognition approaches, e.g. based on PCA or LBP, have been bench-marked mostly on the web-collected IIITD plastic surgery face database. Generally, significant performance drops were reported when comparing facial images taken before and after plastic surgeries. On the one side, some researchers reported problems with said plastic surgery database, i.e. the presence of low quality images. On the other side, the applied methods no longer reflect the state-of-the-art in face recognition. This calls for evaluating the impact of plastic surgery on state-of-the-art deep face recognition systems anew considering high quality imagery of most relevant plastic surgeries.This work introduces the new Hochschule Darmstadt (HDA) plastic surgery database of facial images taken before and after surgery. This database vastly complies with the quality req...

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

Artificial Intelligence Review

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.

Implementation of Plastic Surgery Face Recognition Using Multimodal Biometric Features

Plastic surgery procedures provide a proficient and enduring way to enhance the facial appearance by correcting feature anomalies and treating facial skin to get a younger look. When an individual undergoes plastic surgery, the facial features are reconstructed either globally or locally. However, the variations introduced by plastic surgery remain difficult to be modelled by existing face recognition systems and degrade the performances of face recognition algorithm. Therefore Facial plastic surgery changes facial features to large extend and thus creating a major problem to face recognition system This paper proposes a new Multimodal Biometric approach using principle component analysis and local binary pattern feature extraction algorithm cascaded with periocular feature for plastic surgery invariant face recognition. This method capable of extracting shape as well as texture features and improve the recognition rate using periocular biometric. The experiments conducted using non...

Past, Present, and Future of Face Recognition: A Review

Electronics

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to ...