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eyelid, eyebrows, mouth and lips have a more pronounced effect on face recognition systems than the techniques which deal with ears, mole removal, and Dermabrasion. III. PLASTIC SURGERY AND FACE RECOGNITION Most of the existing face recognition algorithms have predominantly focused on mitigating the effects of pose, illumination and expression, and no attempt has been made to study the effect of local and global plastic surgery on face recognition. As facial plastic surgery procedures become more and more prevalent, face recognition systems will be challenged to recognize individuals after plastic surgery has been performed. In this section, we investigate different aspects related to plastic surgery and face recognition. Specifically, a plastic surgery face database is prepared and performance of six face recognition algorithms is evaluated. 1These images are provided by The American Society for Aesthetic Plastic Surgery SUBMITTED TO IEEE TIFS 3 (a) (b) (c) (d) Before Surgery After Surgery Fig. 1. Illustrating the example of (a) lip augmentation, (b) Otoplasty or ear surgery, (c) liposubmental chin implant and liposuction of chin/neck, and (d) face resurfacing. A. Plastic Surgery Database One of the major challenge in this research is to prepare a database that contains images of individuals before and after facial plastic surgery. There are several concerns in collecting the database as patients are hesitant in sharing their images. Apart from the issues related to privacy, many who have undergone a disease correcting facial surgery would like to be discrete. To the best of our knowledge, there is no publically available facial plastic surgery database that can be used to evaluate current face recognition algorithms or develop a new algorithm. However, to conduct a scientific experimental study and to analyze the effect of both local and global plastic surgery on face recognition, it is imperative to collect face images before and after plastic surgery. Inspired from the data collection procedure of the Public Figures face database [12], we downloaded real world pre and post surgery images mainly from two websites2 . These websites contain images of face as well as non-face plastic surgery procedures. From these images, we manually filtered non-face images along with occluded or partial face images. In total, plastic surgery database thus consists of 1800 full frontal face images pertaining to 900 subjects3 . The database contains a wide variety of cases such as Rhinoplasty (nose surgery), Blepharoplasty (eyelid surgery), brow lift, skin peeling, and Rhytidectomy (face lift). Table I shows the details of images in the plastic surgery database covering different types of surgery. For each individual, there are two frontal face images with proper 2www.locateadoc.com, www.surgery.org. 3A list of URLs to these images is available at www.iiitd.edu.in/iab/ps.html. Type Plastic Surgery Procedure Number of Individuals Local Dermabrasion 32 Brow lift (Forehead surgery) 60 Otoplasty (Ear surgery) 74 Blepharoplasty (Eyelid surgery) 105 Rhinoplasty (Nose surgery) 192 Others (Mentoplasty, Malar augmentation, Craniofacial, Lip augmentation, Fat injection) 56 Global Skin peeling (Skin resurfacing) 73 Rhytidectomy (Face lift) 308 TABLE I DETAILS OF THE PLASTIC SURGERY DATABASE THAT CONTAINS 1800 IMAGES PERTAINING TO 900 SUBJECTS (FOR EACH INDIVIDUAL, ONE PRE SURGERY AND ONE POST SURGERY IMAGE). illumination and neutral expression: the first is taken before surgery and the second is taken after surgery. The database contains 519 image pairs corresponding to local surgeries and 381 cases of global surgery (e.g., skin peeling and face lift). Viola Jones face detector [13] is then used to detect facial region in the images and the size of detected and normalized face images is 200 Ă— 200. B. Algorithms for Evaluation To study the effect of plastic surgery on face recognition, we selected six recognition algorithms. These algorithms are: Principal Component Analysis (PCA) [14], Fisher Discriminant Analysis (FDA) [14], Local Feature Analysis (LFA) [15], Circular Local SUBMITTED TO IEEE TIFS 4 Fig. 2. Samples from the non-surgery face database. Binary Pattern (CLBP) [16], [17], Speeded Up Robust Features (SURF) [18], and Neural Network Architecture based 2D Log Polar Gabor Transform (GNN) [11]. PCA and FDA are appearance-based algorithms, LFA is a feature-based algorithm, SURF is a descriptor based approach, and LBP and GNN are texture-based algorithms. These algorithms are chosen for evaluation because they cover a spectrum of local and global recognition approaches in face recognition literature. C. Experimental Evaluation The experiments are divided into three sets: 1) Performance on the Non-Surgery Database: To analyze the effect of plastic surgery on face recognition algorithms, it is important to have the baseline performance on a dataset that is similar to the plastic surgery database in terms of pose, expression and illumination and does not have plastic surgery variations. Therefore, the database for the first experiment comprises of images from publically available non-surgery databases. 1800 frontal face images with neutral expression, proper illumination, and no occlusion, pertaining to 900 subjects are collected from the AR [19], CMU PIE [20], Georgia Tech [21], GTAV [22] and the FERET [23]
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