Evolutionary granular approach for recognizing faces altered due to plastic surgery (original) (raw)
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Using a Multi-objective evolutionary granular algorithm is proposed to match face images before and after plastic surgery. The algorithm first generates non-disjoint face granules at multiple levels of granularity. The granular information is assimilated using a multiobjective genetic approach that simultaneously optimizes the selection of feature extractor for each face granule along with the weights of individual granules. On the plastic surgery face database, the proposed algorithm yields high identification accuracy as compared to existing algorithms and a commercial face recognition system. Our evaluation results obtained using genetic algorithm with data sets.
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.
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.
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.
Pattern Recognition of Surgically Altered Face Images Using Multi- Objective Evolutionary Algorithm
Citation/Export MLA Leena Patil, Sana Deshmukh, Rakhi Mahajan, Utkarsha Narkhede, “Pattern Recognition of Surgically Altered Face Images Using Multi-Objective Evolutionary Algorithm”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1642 - 1645, DOI: 10.17762/ijritcc2321-8169.1503162 APA Leena Patil, Sana Deshmukh, Rakhi Mahajan, Utkarsha Narkhede, March 15 Volume 3 Issue 3, “Pattern Recognition of Surgically Altered Face Images Using Multi-Objective Evolutionary Algorithm”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1642 - 1645, DOI: 10.17762/ijritcc2321-8169.1503162
Multimodal Biometric Feature Recognition Using Genetic Algorithm
Face recognition algorithms have important applications in various fields like medical field and security. Even so such systems face grave challenges, one of which is the increasing trend of altering facial appearance using biometrics to evade identification. Altering facial appearance using surgically procedures that has risen challenges for face recognition algorithms. Plastic surgery procedures induce nonlinear variations in face which are difficult to recognize. However the nonlinear variations caused by plastic surgery remain difficult to be modelled by existing face recognition systems. In this research focuses on analysing the effect of plastic surgery in face recognition algorithms and multimodal bio-metric feature extractor algorithm is proposed to match face images before and after plastic surgery.