Recognizing Disguised Faces: Human and Machine Evaluation

By Tejas I. Dhamecha, Richa Singh, Mayank Vatsa, Ajay Kumar. 2014

121 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ramachandra Raghavendra
2. Christoph Busch
3. Sébastien Marcel
4. Dacheng Tao
5. Jing Zhang
6. Anjith George

In Signal Processing

1. Ramachandra Raghavendra
2. Christoph Busch
3. Sébastien Marcel
4. Anjith George
5. Dacheng Tao
6. Jing Zhang

Citations in the corpus

• Detection and Continual Learning of Novel Face Presentation Attacks (2021, neutral): “Pavlidis Symosek [7] 2000 − Facial disguises 3DMAD [5] 2013 17 3D mask attacks I(2)BVSD [4] 2013 75 Facial disguises GUC-LiFFAD [8] 2015 80 2D print and replay MS-Spoof [3] 2015 21 2D print BRSU [11] 2016 50 3D masks EMSPAD [9] 2017 50 2D print MLFP [1] 2017 10 2D & 3D masks CASIA-SURF [12] 2020 100”
• Presentation Attack Detection Methods for Face Recognition Systems (2017, methodology): “Examples of face disguise PAI (taken from IIITD face disguise database [Dhamecha et al. 2014]).”
• CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, unknown): “I2BVSD [14] 2013 75 681∗ VIS/Thermal RGB/Heat 3D Mask”