IEEE Signal Processing Magazine - January 2018 - 66
DEEP LEARNING FOR VISUAL UNDERSTANDING:
PART 2
Rajeev Ranjan, Swami Sankaranarayanan, Ankan Bansal,
Navaneeth Bodla, Jun-Cheng Chen, Vishal M. Patel,
Carlos D. Castillo, and Rama Chellappa
Deep Learning for Understanding Faces
Machines may be just as good, or better, than humans
R
ecent developments in deep convolutional neural networks
(DCNNs) have shown impressive performance improvements on various object detection/recognition problems.
This has been made possible due to the availability of large annotated data and a better understanding of the nonlinear mapping
between images and class labels, as well as the affordability of
powerful graphics processing units (GPUs). These developments
in deep learning have also improved the capabilities of machines
in understanding faces and automatically executing the tasks
of face detection, pose estimation, landmark localization, and
face recognition from unconstrained images and videos. In this
article, we provide an overview of deep-learning methods used
for face recognition. We discuss different modules involved in
designing an automatic face recognition system and the role of
deep learning for each of them. Some open issues regarding
DCNNs for face recognition problems are then discussed. This
article should prove valuable to scientists, engineers, and end
users working in the fields of face recognition, security, visual
surveillance, and biometrics.
What we can learn from faces
©ISTOCKPHOTO.COM/ZAPP2PHOTO
Digital Object Identifier 10.1109/MSP.2017.2764116
Date of publication: 9 January 2018
66
Facial analytics is a challenging problem in computer vision and
has been actively researched for more than two decades [1]. The
goal is to extract as much information as possible, such as location, pose, gender, ID, age, emotion, and so forth from a face.
Applications of this technology include detecting and identifying
a person of interest from surveillance videos, active authentication of users' cell phones, payment transactions using face biometrics, smart cars, etc. In addition, there has been a growing
interest in face recognition and verification from unconstrained
images and videos that also involve subtasks such as face detection, facial landmark localization, etc.
In this article, we present an overview of recent automatic
face identification and verification systems based on deep
learning. Three modules are typically needed for such an automatic system. First, a face detector is applied to localize faces in
images or videos. For a robust system, the face detector should
be capable of detecting faces with varying pose, illumination,
IEEE SIGNAL PROCESSING MAGAZINE
|
January 2018
|
1053-5888/18©2018IEEE
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