4,187 research outputs found
Multi-view Face Detection Using Deep Convolutional Neural Networks
In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR
Asymmetric Pruning for Learning Cascade Detectors
Cascade classifiers are one of the most important contributions to real-time
object detection. Nonetheless, there are many challenging problems arising in
training cascade detectors. One common issue is that the node classifier is
trained with a symmetric classifier. Having a low misclassification error rate
does not guarantee an optimal node learning goal in cascade classifiers, i.e.,
an extremely high detection rate with a moderate false positive rate. In this
work, we present a new approach to train an effective node classifier in a
cascade detector. The algorithm is based on two key observations: 1) Redundant
weak classifiers can be safely discarded; 2) The final detector should satisfy
the asymmetric learning objective of the cascade architecture. To achieve this,
we separate the classifier training into two steps: finding a pool of
discriminative weak classifiers/features and training the final classifier by
pruning weak classifiers which contribute little to the asymmetric learning
criterion (asymmetric classifier construction). Our model reduction approach
helps accelerate the learning time while achieving the pre-determined learning
objective. Experimental results on both face and car data sets verify the
effectiveness of the proposed algorithm. On the FDDB face data sets, our
approach achieves the state-of-the-art performance, which demonstrates the
advantage of our approach.Comment: 14 page
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"
Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose a two-stage cascaded face detection framework, Multi-Path
Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a
deep neural network with a classic learning strategy, to tackle this challenge.
The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes
faces at three different scales. It simultaneously utilizes three parallel
outputs of the convolutional feature maps to predict multi-scale candidate face
regions. The "atrous" convolution trick (convolution with up-sampled filters)
and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN
to further boost its performance. The second stage is a Boosted Forests
classifier, which utilizes deep facial features pooled from inside the
candidate face regions as well as deep contextual features pooled from a larger
region surrounding the candidate face regions. This step is included to further
remove hard negative samples. Experiments show that this approach achieves
state-of-the-art face detection performance on the WIDER FACE dataset "hard"
partition, outperforming the former best result by 9.6% for the Average
Precision.Comment: 11 pages, 7 figures, to be presented at CRV 201
Face Detection with Effective Feature Extraction
There is an abundant literature on face detection due to its important role
in many vision applications. Since Viola and Jones proposed the first real-time
AdaBoost based face detector, Haar-like features have been adopted as the
method of choice for frontal face detection. In this work, we show that simple
features other than Haar-like features can also be applied for training an
effective face detector. Since, single feature is not discriminative enough to
separate faces from difficult non-faces, we further improve the generalization
performance of our simple features by introducing feature co-occurrences. We
demonstrate that our proposed features yield a performance improvement compared
to Haar-like features. In addition, our findings indicate that features play a
crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision
201
Asymmetric Totally-corrective Boosting for Real-time Object Detection
Real-time object detection is one of the core problems in computer vision.
The cascade boosting framework proposed by Viola and Jones has become the
standard for this problem. In this framework, the learning goal for each node
is asymmetric, which is required to achieve a high detection rate and a
moderate false positive rate. We develop new boosting algorithms to address
this asymmetric learning problem. We show that our methods explicitly optimize
asymmetric loss objectives in a totally corrective fashion. The methods are
totally corrective in the sense that the coefficients of all selected weak
classifiers are updated at each iteration. In contract, conventional boosting
like AdaBoost is stage-wise in that only the current weak classifier's
coefficient is updated. At the heart of the totally corrective boosting is the
column generation technique. Experiments on face detection show that our
methods outperform the state-of-the-art asymmetric boosting methods.Comment: 14 pages, published in Asian Conf. Computer Vision 201
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