2,901 research outputs found
From Facial Parts Responses to Face Detection: A Deep Learning Approach
In this paper, we propose a novel deep convolutional network (DCN) that
achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically,
our method achieves a high recall rate of 90.99% on the challenging FDDB
benchmark, outperforming the state-of-the-art method by a large margin of
2.91%. Importantly, we consider finding faces from a new perspective through
scoring facial parts responses by their spatial structure and arrangement. The
scoring mechanism is carefully formulated considering challenging cases where
faces are only partially visible. This consideration allows our network to
detect faces under severe occlusion and unconstrained pose variation, which are
the main difficulty and bottleneck of most existing face detection approaches.
We show that despite the use of DCN, our network can achieve practical runtime
speed.Comment: To appear in ICCV 201
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
On Detecting Faces And Classifying Facial Races With Partial Occlusions And Pose Variations
In this dissertation, we present our contributions in face detection and facial race classification.
Face detection in unconstrained images is a traditional problem in computer vision community. Challenges still remain. In particular, the detection of partially occluded faces with pose variations has not been well addressed. In the first part of this dissertation, our contributions are three-fold. First, we introduce our four image datasets consisting of large-scale labeled face dataset, noisy large-scale labeled non-face dataset, CrowdFaces dataset, and CrowdNonFaces dataset intended to be used for face detection training. Second, we improve Viola-Jones (VJ) face detection results by first training a Convolutional Neural Network (CNN) model on our noisy datasets. We show our improvement over the VJ face detector on AFW face detection benchmark dataset. However, existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. Hence, we thirdly propose our Large-Scale Deep Learning (LSDL), a method that does not require training several CNN models or hand-crafted features computations to detect faces. Our LSDL face detector is trained on a single CNN model to detect unconstrained multi-view partially occluded and non-partially occluded faces. The model is trained with a large number of face training examples that cover most partial occlusions and non-partial occlusions facial appearances. The LSDL face detection method is achieved by selecting detection windows with the highest confidence scores using a threshold. Our evaluation results show that our LSDL method achieves the best performance on AFW dataset and a comparable performance on FDDB dataset among state-of-the-art face detection methods without manually extending or adjusting the square detection bounding boxes.
Many biometrics and security systems use facial information to obtain an individual identification and recognition. Classifying a race from a face image can provide a strong hint to search for facial identity and criminal identification. Current facial race classification methods are confined only to constrained non-partially occluded frontal faces. Challenges remain under unconstrained environments such as partial occlusions and pose variations, low illuminations, and small scales. In the second part of the dissertation, we propose a CNN model to classify facial races with partial occlusions and pose variations. The proposed model is trained using a broad and balanced racial distributed face image dataset. The model is trained on four major human races, Caucasian, Indian, Mongolian, and Negroid. Our model is evaluated against the state-of-the-art methods on a constrained face test dataset. Also, an evaluation of the proposed model and human performance is conducted and compared on our new unconstrained facial race benchmark (CIMN) dataset. Our results show that our model achieves 95.1% of race classification accuracy in the constrained environment. Furthermore, the model achieves a comparable accuracy of race classification compared to human performance on the current challenges in the unconstrained environment
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
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