A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research

Literature review.

<p>Existing algorithms for addressing disguise variations. AR database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099212#pone.0099212-Martinez1" target="_blank">[33]</a> contains 3200+ images pertaining to 126 subjects with two kinds of disguises (sunglasses and scarves). The National Geographic (NG) dataset contains 46 images of 1 individual, with various accessories such as hat, glasses, sunglasses, and facial hair. *Private dataset of 150 images pertaining to 15 individuals which contains similar real and synthetic disguise variations as in NG dataset. Synthetic disguise dataset of 4000 images pertaining to 100 individuals. Private datasets are collected by researches in real world scenarios from ATM (automatic teller machine) kiosk.</p

Age and gender distribution of participants in the four sets.

<p>The results reported are mean values with standard deviation.</p

The results of the proposed face recognition framework using LBP descriptor.

<p>The results of the proposed face recognition framework using LBP descriptor.</p

ROC curves for patch classification.

<p>ROC curves for patch classification.</p

Distribution of genuine and impostor pairs in questionnaires.

<p>Distribution of genuine and impostor pairs in questionnaires.</p

Recognizing Disguised Faces: Human and Machine Evaluation

<div><p>Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces. Performance is also evaluated under familiarity and match/mismatch with the ethnicity of observers. The findings of this study are used to develop an automated algorithm to verify the faces presented under disguise variations. We use automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy. The performance of the proposed algorithm is evaluated on the IIIT-Delhi Disguise database that contains images pertaining to 75 subjects with different kinds of disguise variations. The experiments suggest that the proposed algorithm can outperform a popular commercial system and evaluates them against humans in matching disguised face images.</p></div

Confusion matrix for comparing the consistency of Set FS-I and Set FS-II.

<p> and represent the genuine and impostor classified samples respectively. The numbers in every cell represent the co-occurrence of decisions (correct/incorrect). For example, block shows that for 227 image pairs, participants in both Set FS-I and Set FS-II responded that they were genuine pairs.</p

Sample questionnaire.

<p>Sample questionnaire.</p

Illustrating the steps involved in the proposed face recognition framework.

<p>Illustrating the steps involved in the proposed face recognition framework.</p