124,543 research outputs found
Video-based driver identification using local appearance face recognition
In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms — Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1
On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly
In the field of face recognition, Sparse Representation (SR) has received
considerable attention during the past few years. Most of the relevant
literature focuses on holistic descriptors in closed-set identification
applications. The underlying assumption in SR-based methods is that each class
in the gallery has sufficient samples and the query lies on the subspace
spanned by the gallery of the same class. Unfortunately, such assumption is
easily violated in the more challenging face verification scenario, where an
algorithm is required to determine if two faces (where one or both have not
been seen before) belong to the same person. In this paper, we first discuss
why previous attempts with SR might not be applicable to verification problems.
We then propose an alternative approach to face verification via SR.
Specifically, we propose to use explicit SR encoding on local image patches
rather than the entire face. The obtained sparse signals are pooled via
averaging to form multiple region descriptors, which are then concatenated to
form an overall face descriptor. Due to the deliberate loss spatial relations
within each region (caused by averaging), the resulting descriptor is robust to
misalignment & various image deformations. Within the proposed framework, we
evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder
Neural Network (SANN), and an implicit probabilistic technique based on
Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and
ChokePoint datasets show that the proposed local SR approach obtains
considerably better and more robust performance than several previous
state-of-the-art holistic SR methods, in both verification and closed-set
identification problems. The experiments also show that l1-minimisation based
encoding has a considerably higher computational than the other techniques, but
leads to higher recognition rates
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
We consider the image classification problem via kernel collaborative
representation classification with locality constrained dictionary (KCRC-LCD).
Specifically, we propose a kernel collaborative representation classification
(KCRC) approach in which kernel method is used to improve the discrimination
ability of collaborative representation classification (CRC). We then measure
the similarities between the query and atoms in the global dictionary in order
to construct a locality constrained dictionary (LCD) for KCRC. In addition, we
discuss several similarity measure approaches in LCD and further present a
simple yet effective unified similarity measure whose superiority is validated
in experiments. There are several appealing aspects associated with LCD. First,
LCD can be nicely incorporated under the framework of KCRC. The LCD similarity
measure can be kernelized under KCRC, which theoretically links CRC and LCD
under the kernel method. Second, KCRC-LCD becomes more scalable to both the
training set size and the feature dimension. Example shows that KCRC is able to
perfectly classify data with certain distribution, while conventional CRC fails
completely. Comprehensive experiments on many public datasets also show that
KCRC-LCD is a robust discriminative classifier with both excellent performance
and good scalability, being comparable or outperforming many other
state-of-the-art approaches
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