135 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
A hybrid technique for face detection in color images
In this paper, a hybrid technique for face detection in color images is presented. The proposed technique combines three analysis models, namely skin detection, automatic eye localization, and appearance-based face/nonface classification. Using a robust histogram-based skin detection model, skin-like pixels are first identified in the RGB color space. Based on this, face bounding-boxes are extracted from the image. On detecting a face bounding-box, approximate positions of the candidate mouth feature points are identified using the redness property of image pixels. A region-based eye localization step, based on the detected mouth feature points, is then applied to face bounding-boxes to locate possible eye feature points in the image. Based on the distance between the detected eye feature points, face/non-face classification is performed over a normalized search area using the Bayesian discriminating feature (BDF) analysis method. Some subjective evaluation results are presented on images taken using digital cameras and a Webcam, representing both indoor and outdoor scenes
Profile approach for recognition of three-dimensional magnetic structures
We propose an approach for low-dimensional visualisation and classification
of complex topological magnetic structures formed in magnetic materials. Within
the approach one converts a three-dimensional magnetic configuration to a
vector containing the only components of the spins that are parallel to the z
axis. The next crucial step is to sort the vector elements in ascending or
descending order. Having visualized profiles of the sorted spin vectors one can
distinguish configurations belonging to different phases even with the same
total magnetization. For instance, spin spiral and paramagnetic states with
zero total magnetic moment can be easily identified. Being combined with a
simplest neural network our profile approach provides a very accurate phase
classification for three-dimensional magnets characterized by complex
multispiral states even in the critical areas close to phases transitions. By
the example of the skyrmionic configurations we show that profile approach can
be used to separate the states belonging to the same phase
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
We present an integrated framework for using Convolutional Networks for
classification, localization and detection. We show how a multiscale and
sliding window approach can be efficiently implemented within a ConvNet. We
also introduce a novel deep learning approach to localization by learning to
predict object boundaries. Bounding boxes are then accumulated rather than
suppressed in order to increase detection confidence. We show that different
tasks can be learned simultaneously using a single shared network. This
integrated framework is the winner of the localization task of the ImageNet
Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very
competitive results for the detection and classifications tasks. In
post-competition work, we establish a new state of the art for the detection
task. Finally, we release a feature extractor from our best model called
OverFeat
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