3 research outputs found
Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)
We propose a new approach to video face recognition. Our component-wise
feature aggregation network (C-FAN) accepts a set of face images of a subject
as an input, and outputs a single feature vector as the face representation of
the set for the recognition task. The whole network is trained in two steps:
(i) train a base CNN for still image face recognition; (ii) add an aggregation
module to the base network to learn the quality value for each feature
component, which adaptively aggregates deep feature vectors into a single
vector to represent the face in a video. C-FAN automatically learns to retain
salient face features with high quality scores while suppressing features with
low quality scores. The experimental results on three benchmark datasets,
YouTube Faces, IJB-A, and IJB-S show that the proposed C-FAN network is capable
of generating a compact feature vector with 512 dimensions for a video sequence
by efficiently aggregating feature vectors of all the video frames to achieve
state of the art performance
Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
Face recognition has made significant progress in recent years due to deep
convolutional neural networks (CNN). In many face recognition (FR) scenarios,
face images are acquired from a sequence with huge intra-variations. These
intra-variations, which are mainly affected by the low-quality face images,
cause instability of recognition performance. Previous works have focused on
ad-hoc methods to select frames from a video or use face image quality
assessment (FIQA) methods, which consider only a particular or combination of
several distortions.
In this work, we present an efficient non-reference image quality assessment
for FR that directly links image quality assessment (IQA) and FR. More
specifically, we propose a new measurement to evaluate image quality without
any reference. Based on the proposed quality measurement, we propose a deep
Tiny Face Quality network (tinyFQnet) to learn a quality prediction function
from data.
We evaluate the proposed method for different powerful FR models on two
classical video-based (or template-based) benchmark: IJB-B and YTF. Extensive
experiments show that, although the tinyFQnet is much smaller than the others,
the proposed method outperforms state-of-the-art quality assessment methods in
terms of effectiveness and efficiency
A Comprehensive Overview of Biometric Fusion
The performance of a biometric system that relies on a single biometric
modality (e.g., fingerprints only) is often stymied by various factors such as
poor data quality or limited scalability. Multibiometric systems utilize the
principle of fusion to combine information from multiple sources in order to
improve recognition accuracy whilst addressing some of the limitations of
single-biometric systems. The past two decades have witnessed the development
of a large number of biometric fusion schemes. This paper presents an overview
of biometric fusion with specific focus on three questions: what to fuse, when
to fuse, and how to fuse. A comprehensive review of techniques incorporating
ancillary information in the biometric recognition pipeline is also presented.
In this regard, the following topics are discussed: (i) incorporating data
quality in the biometric recognition pipeline; (ii) combining soft biometric
attributes with primary biometric identifiers; (iii) utilizing contextual
information to improve biometric recognition accuracy; and (iv) performing
continuous authentication using ancillary information. In addition, the use of
information fusion principles for presentation attack detection and
multibiometric cryptosystems is also discussed. Finally, some of the research
challenges in biometric fusion are enumerated. The purpose of this article is
to provide readers a comprehensive overview of the role of information fusion
in biometrics.Comment: Accepted for publication in Information Fusio