Recognizing human faces under disguise and makeup

Abstract

The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition. We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.

Data Augmentation-Assisted Makeup-Invariant Face Recognition

“…Hand-crafted feature based approaches extract local or global features from query and gallery faces to perform matching. The representative works in this category include [4,7,8,[12][13][14]. In [4], authors extracted LBP and Gabor features to capture micropatterns and shape and texture information, respectively.…”

“…In [4], authors extracted LBP and Gabor features to capture micropatterns and shape and texture information, respectively. Wang and Kumar [7] used LBP with biometric and nonbiometric blocks. Matching is performed using only the biometric blocks to achieve superior accuracies.…”

“…Keeping in view the substantial ability of makeup to change facial appearance, a number of approaches attempted to achieve makeup-invariant face recognition. Most of the existing methods such as [4,7,8] attempt to extract local features from face images to match query faces with gallery. In most of these methods, hand-crafted features like local binary patterns (LBP) Gabor wavelets, and other local and global features.…”

Detecting sensor level spoof attacks using joint encoding of temporal and spatial features

“…Therefore, the sparse encoding representation, which ensures constraint on the training dictionary, can be formulated as follows: (6) where  is the regularization parameter. In this manner, the spoof face sample detection problem can be considered as a solution to the two-class classification problem.…”

“…This is largely due to the fact that intruders are always expected to imitate these unique biometric details that would lead to positive but undesirable identification by the biometrics system. Successful examples of anti-spoofing results [2] are available for a range of deployed biometrics systems with different modalities: fingerprint spoofing detection [3]- [4], face spoofing detection [5], [12], facial disguise and makeup detection [6], [15], etc. The development of effective anti-spoofing techniques is critical to safeguard the integrity of deployed biometrics systems.…”