In this paper, we propose a convolutional neural
network (CNN) based, scenario-dependent and sensor (mobile
device) adaptable hierarchical classification framework. Our
proposed framework is designed to automatically categorize
face data captured under various challenging conditions, before
the FR algorithms (pre-processing, feature extraction and
matching) are used. First, a unique multi-sensor database (using
Samsung S4 Zoom, Nokia 1020, iPhone 5S and Samsung S5
phones) is collected containing face images indoors, outdoors,
with yaw angle from -90 to +90 and at two different distances,
i.e. 1 and 10 meters. To cope with pose variations, face detection
and pose estimation algorithms are used for classifying the
facial images into a frontal or a non-frontal class. Next,
our proposed framework is used where tri-level hierarchical
classification is performed as follows: Level 1, face images are
classified based on phone type; Level 2, face images are further
classified into indoor and outdoor images; and finally, Level
3 face images are classified into a close (1m) and a far, low
quality, (10m) distance categories respectively. Experimental
results show that classification accuracy is scenario dependent,
reaching from 95 to more than 98% accuracy for level 2
and from 90 to more than 99% for level 3 classification. A
set of experiments is performed indicating that, the usage of
data grouping before the face matching is performed, resulted
in a significantly improved rank-1 identification rate when
compared to the original (all vs. all) biometric system.
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On matching skulls to digital face images: A preliminary approach
Forensic application of automatically matching skull
with face images is an important research area linking biometrics
with practical applications in forensics. It is an opportunity
for biometrics and face recognition researchers
to help the law enforcement and forensic experts in giving
an identity to unidentified human skulls. It is an extremely
challenging problem which is further exacerbated due to
lack of any publicly available database related to this problem.
This is the first research in this direction with a twofold
contribution: (i) introducing the first of its kind skullface
image pair database, IdentifyMe, and (ii) presenting a
preliminary approach using the proposed semi-supervised
formulation of transform learning. The experimental results
and comparison with existing algorithms showcase the
challenging nature of the problem. We assert that the availability
of the database will inspire researchers to build sophisticated
skull-to-face matching algorithms.
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- NSF-PAR ID:
- 10053780
- Date Published:
- Journal Name:
- International Joint Conference on Biometrics (IJCB)
- Page Range / eLocation ID:
- 813 to 819
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Performing a direct match between images from different spectra (i.e., passive infrared and visible) is challenging because each spectrum contains different information pertaining to the subject’s face. In this work, we investigate the benefits and limitations of using synthesized visible face images from thermal ones and vice versa in cross-spectral face recognition systems. For this purpose, we propose utilizing canonical correlation analysis (CCA) and manifold learning dimensionality reduction (LLE). There are four primary contributions of this work. First, we formulate the cross-spectral heterogeneous face matching problem (visible to passive IR) using an image synthesis framework. Second, a new processed database composed of two datasets consistent of separate controlled frontal face subsets (VIS-MWIR and VIS-LWIR) is generated from the original, raw face datasets collected in three different bands (visible, MWIR and LWIR). This multi-band database is constructed using three different methods for preprocessing face images before feature extraction methods are applied. There are: (1) face detection, (2) CSU’s geometric normalization, and (3) our recommended geometric normalization method. Third, a post-synthesis image denoising methodology is applied, which helps alleviate different noise patterns present in synthesized images and improve baseline FR accuracy (i.e. before image synthesis and denoising is applied) in practical heterogeneous FR scenarios. Finally, an extensive experimental study is performed to demonstrate the feasibility and benefits of cross-spectral matching when using our image synthesis and denoising approach. Our results are also compared to a baseline commercial matcher and various academic matchers provided by the CSU’s Face Identification Evaluation System.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BTAS.2017.8272775
