Matching Forensic Sketches to Mug Shot Photos

Authors:
Brendan Klare;Zhifeng Li;Anil K. Jain
Affiliations:
Michigan State University, East Lansing;Michigan State University, East Lansing;Michigan State University, East Lansing and Korea University, Seoul
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2011

Citing 0
Cited 7

Face image retrieval based on probe sketch using SIFT feature descriptors

PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Face photo retrieval by sketch example

Proceedings of the 20th ACM international conference on Multimedia
Heterogeneous image transformation

Pattern Recognition Letters
Multi-view discriminant analysis

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Recognizing the royals: leveraging computerized face recognition for identifying subjects in ancient artworks

Proceedings of the 21st ACM international conference on Multimedia
Multi-feature canonical correlation analysis for face photo-sketch image retrieval

Proceedings of the 21st ACM international conference on Multimedia
Sketched symbol recognition using Latent-Dynamic Conditional Random Fields and distance-based clustering

Pattern Recognition

Quantified Score

Hi-index	0.14

Visualization

Abstract

The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, we present a framework called local feature-based discriminant analysis (LFDA). In LFDA, we individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns (MLBP). Multiple discriminant projections are then used on partitioned vectors of the feature-based representation for minimum distance matching. We apply this method to match a data set of 159 forensic sketches against a mug shot gallery containing 10,159 images. Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images. We were able to further improve the matching performance using race and gender information to reduce the target gallery size. Additional experiments demonstrate that the proposed framework leads to state-of-the-art accuracys when matching viewed sketches.