  Facial recognition has always been a challeng- ing task for computer vision
scientists and experts. Despite complexities arising due to variations in
camera parameters, illumination and face orientations, significant progress has
been made in the field with deep learning algorithms now competing with
human-level accuracy. But in contrast to the recent advances in face
recognition techniques, Disguised Facial Identification continues to be a
tougher challenge in the field of computer vision. The modern day scenario,
where security is of prime concern, regular face identification techniques do
not perform as required when the faces are disguised, which calls for a
different approach to handle situations where intruders have their faces
masked. Along the same lines, we propose a deep learning architecture for
disguised facial recognition (DFR). The algorithm put forward in this paper
detects 20 facial key-points in the first stage, using a 14-layered
convolutional neural network (CNN). These facial key-points are later utilized
by a support vector machine (SVM) for classifying the disguised faces based on
the euclidean distance ratios and angles between different facial key-points.
This overall architecture imparts a basic intelligence to our system. Our
key-point feature prediction accuracy is 65% while the classification rate is
72.4%. Moreover, the architecture works at 19 FPS, thereby performing in almost
real-time. The efficiency of our approach is also compared with the
state-of-the-art Disguised Facial Identification methods.
