Partial Face Recognition: Alignment-Free Approach

Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 5; pp. 1193 - 1205
Main Authors: Shengcai Liao, Jain, A. K., Li, S. Z.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2013
IEEE Computer Society
Subjects:
Algorithms
alignment free
Applied sciences
Artificial intelligence
Biometric Identification - methods
Computer science; control theory; systems
Databases, Factual
Detection, estimation, filtering, equalization, prediction
Detectors
Exact sciences and technology
Face
Face - anatomy & histology
Face recognition
Histograms
Humans
Image edge detection
Image Processing, Computer-Assisted
Information, signal and communications theory
keypoint descriptor
Lighting
open-set identification
Partial face recognition
Pattern recognition. Digital image processing. Computational geometry
Posture
Robustness
Signal and communications theory
Signal, noise
sparse representation
Telecommunications and information theory
Gabor filter
Mobile phone
Computer vision
Dictionaries
Image processing
Face recognition
Image content
alignment free
open-set identification
Image databank
Autoregressive model
Regression analysis
Alignment
Image analysis
Experimental result
Partial face recognition
Authentication
Facies
Sparse representation
Content analysis
keypoint descriptor
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld devices (e.g., mobile phones) in particular. In this paper, we propose a general partial face recognition approach that does not require face alignment by eye coordinates or any other fiducial points. We develop an alignment-free face representation method based on Multi-Keypoint Descriptors (MKD), where the descriptor size of a face is determined by the actual content of the image. In this way, any probe face image, holistic or partial, can be sparsely represented by a large dictionary of gallery descriptors. A new keypoint descriptor called Gabor Ternary Pattern (GTP) is also developed for robust and discriminative face recognition. Experimental results are reported on four public domain face databases (FRGCv2.0, AR, LFW, and PubFig) under both the open-set identification and verification scenarios. Comparisons with two leading commercial face recognition SDKs (PittPatt and FaceVACS) and two baseline algorithms (PCA+LDA and LBP) show that the proposed method, overall, is superior in recognizing both holistic and partial faces without requiring alignment.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2012.191