2.11 CMC curves showing the performance of sketch to digital face image matching. algorithms on the CUHK database... 40

Transcription

1 List of Figures 1.1 Illustrating different stages in a face recognition system i.e. image acquisition, face detection, face normalization, feature extraction, and matching Illustrating the concepts of inter-class and intra-class variations in biometrics Covariates of face recognition: (a) existing covariates and (b) emerging covariates Examples showing exaggeration of facial features in forensic sketches Paper quality, sensor noise, and old photographs can affect the quality of sketch-digital image pairs and hence reduce the performance of matching algorithms. (a) Good quality sketch-digital image pairs (CUHK database) and (b) poor quality sketch-digital image pairs (Forensic sketch database) Quality enhancement using the pre-processing technique. (a) represents digital face image before and after pre-processing and(b) represents forensic sketches before and after pre-processing Steps involved in the proposed algorithm for matching sketches with digital face images Illustrating the steps involved in computing the circular WLD histogram (adapted from [1]) Illustrating the steps involved in memetic optimization for assigning optimal weights to each tessellated face region (a) Sample images from the IIIT-Delhi Sketch database. The first row represents the viewed sketches, the second row represents the corresponding digital face images and the third row represents the corresponding semiforensic sketches. (b) Sample images from the CUHK database Sample images from the Forensic Sketch database. Images are obtained from different forensic artists [2], [3]

2 2.9 CMC curves showing the performance of sketch to digital face image matching algorithms on the CUHK database CMC curves showing the performance of sketch to digital face image matching algorithms on the IIIT-Delhi Viewed Sketch database CMC curves showing the performance of sketch to digital face image matching algorithms on the Combined database CMC curves showing the identification performance when algorithms are trained on viewed sketches and matching is performed on semi-forensic sketches CMC curves showing the identification performance when algorithms are trained on viewed sketches and matching is performed on forensic sketches CMC curves showing the identification performance when algorithms are trained on semi-forensic sketches and matching is performed on forensic sketches CMC curves showing the identification performance when algorithms are trained on viewed sketch-digital image pairs and testing is performed using pre-processed (enhanced) forensic sketch-digital image pairs CMC curves showing the identification performance when algorithms are trained on viewed sketch-digital image pairs and tested with large scale digital gallery and forensic sketch probes CMC curves showing the identification performance when algorithms are trained on semi-forensic sketch-digital image pairs and tested with large scale digital (enhanced) gallery and pre-processed forensic sketch probes Illustrating sample cases when (a) the proposed approach and LFDA [4] correctly recognize, (b) LFDA fails while the proposed algorithm correctly recognizes, (c) the proposed algorithm fails while LFDA correctly recognizes, and (d) both the algorithms fail to recognize Facial regions for correctly and incorrectly matched (a) viewed sketches, (b) semi-forensic sketches, and (c) forensic sketches. Dots represents the area that user found to be most discriminating in matching the sketch with digital face images Illustrating the variations in facial appearance, texture, and structural geometry caused due to plastic surgery (images taken from internet)

3 3.2 Relation among plastic surgery, aging, and disguise variations with respect to face recognition Block diagram illustrating different stages of the proposed algorithm Face granules in the first level of granularity. F Gr1,F Gr2, and F Gr3 are generatedbythegaussianoperator, andf Gr4,F Gr5, andf Gr6 aregenerated by the Laplacian operator Horizontal face granules from the second level of granularity (F Gr7 F Gr15 ) Vertical face granules from the second level of granularity (F Gr16 F Gr24 ) (a) Golden ratio face template [5] and (b) face granules in the third level of granularity (F Gr25 F Gr40 ) Genetic optimization process for selecting feature extractor and weight for each face granule CMC curves for the proposed and existing algorithms on the plastic surgery face database CMC curves for the proposed and existing algorithms on the combined heterogeneous face database CMC curves for the proposed and commercial algorithms for large scale evaluation on probe images from (a) Case 1 of Experiment 3 and (b) Case 2 of Experiment CMC curves for the proposed and commercial algorithms for large scale evaluation on probe images from (a) Case 1 of Experiment 3 and (b) Case 2 of Experiment CMC curves on different types of local and global plastic surgery procedures for the proposed algorithm F Gr29 represents the right periocular region and F Gr31 represents the left periocular region CMC curves comparing the performance of different algorithms for matching periocular region on the plastic surgery face database Illustrating the difference in matching (a) low resolution and high resolution images, (b) two high resolution images, and (c) two low resolution images Illustrates the challenge in matching low resolution images when coupled with other covariates. Low resolution challenge (a) alone, (b) with pose, (c) with illumination, and (d) with expression

4 4.3 Broad view of super resolution based approaches for cross-resolution face matching Broad view of transformation based approaches for cross-resolution face matching Illustrating the cross-pollination of transfer learning and co-training for transferring knowledge from source domain to target domain Block diagram illustrating the steps involved in the proposed co-transfer learning framework Block diagram illustrating the training process of the source and target domain classifiers to build the ensembles Block diagram illustrating the co-transfer learning in the target domain with unlabeled probe instances Sample images from the (a) CMU Multi-PIE, (b) SCface, (c) ChokePoint, and (d) MBGC v.2 video challenge databases CMC curves showing the performance for matching probe images with gallery images on the CMU Multi-PIE database CMC curves showing the performance for matching probe images with gallery images on the SCface database CMC curves showing the performance for matching probe images with gallery images on the ChokePoint database CMC curves showing the performance for matching probe images with gallery images on the MBGC v.2 video challenge database Illustrating sample cases when the proposed approach (a) correctly recognizes and (b) fails to recognize. All the examples are with probe(left image) size and gallery (right image) size Illustrates the confidence interval for different algorithms for matching crossresolution face images on the CMU Multi-PIE database Illustrates the confidence interval for different algorithms for matching crossresolution face images on the SCface database Illustrates the confidence interval for different algorithms for matching crossresolution face images on the ChokePoint database Illustrates the confidence interval for different algorithms for matching crossresolution face images on the MBGC v2 video challenge database

5 4.19 Enhanced images obtained using three super-resolution techniques (SR- 1,SR-2, and SR-3). The leftmost column represents low resolution (24 24) images and the rightmost column represents the original high resolution images (72 72) from the (a) CMU Multi-PIE, (b) SCface, (c) ChokePoint, and (d) MBGC v.2 video challenge databases CMC curves comparing the performance of the proposed algorithm with three super resolution techniques on the CMU Multi-PIE database. Probe images of pixels are super-resolved by a magnification factor of 3 to match the gallery resolution of pixels CMC curves comparing the performance of the proposed algorithm with three super resolution techniques on the SCface database. Probe images of pixels are super-resolved by a magnification factor of 3 to match the gallery resolution of pixels CMC curves comparing the performance of the proposed algorithm with three super resolution techniques on the ChokePoint database. Probe images of pixels are super-resolved by a magnification factor of 3 to match the gallery resolution of pixels CMC curves comparing the performance of the proposed algorithm with three super resolution techniques on the MBGC v.2 video challenge database. Probe images of pixels are super-resolved by a magnification factor of 3 to match the gallery resolution of pixels Real world cases for cross-resolution face matching: (a) low resolution probe images and (b) corresponding gallery images Illustrates the abundant information present in videos. Compared to (a) still face images, (b) video frames represent large intra-personal and temporal variations useful for face recognition Illustrates the block diagram of the proposed algorithm for matching two videos Illustrates clustering based re-ranking and fusion to form the video signature. Clustering based re-ranking associates dictionary images to different clusters and adjusts their similarity scores. It facilitates to bring images similar to the query frame towards the top of the ranked list. The lists are then re-ranked using the adjusted scores and are finally combined to generate the video signature

6 5.4 Sample images from the MBGC v2 database (a) still face images, (b) frames from activity video, and (c) frames from walking video Illustrates the variations in equal error rate by varying the number of clusters ROC curves comparing the performance of the proposed algorithm with benchmark results on the YouTube faces database [6]. (Best viewed in color). The results from the YouTube database website are as of October, Illustrating examples when the proposed algorithm correctly classified (a) same, (b) not-same video pairs from the YouTube faces database [6]. Similarly, examples when the proposed algorithm incorrectly classified (c) same and (d) not-same video pairs Illustrates the confidence interval for different algorithms for video based face recognition on the YouTube faces database ROC curves comparing the performance of the proposed algorithm with COTS and MNF on the MBGC v2 database [7] for matching activity and walking videos with the gallery comprising still face images ROC curves comparing the performance of the proposed algorithm with COTS and MNF on the MBGC v2 database [7] for matching still face images with gallery comprising activity and walking videos. (Best viewed in color) ROC curves showing the performance of the proposed algorithm on the MBGC v2 video challenge database [7] for matching walking vs walking (WW) ROC curves showing the performance of the proposed algorithm on the MBGC v2 video challenge database [7] for matching walking vs activity (WA) ROC curves showing the performance of the proposed algorithm on the MBGC v2 video challenge database [7] for matching activity vs activity (AA) videos (Best viewed in color) Progression in face recognition with respect to different covariates