4 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
Acceleration and energy consumption optimization in cascading classifiers for face detection on low-cost ARM big.LITTLE asymmetric architectures
This paper proposes a mechanism to accelerate and optimize the energy
consumption of a face detection software based on Haar-like cascading
classifiers, taking advantage of the features of low-cost Asymmetric Multicore
Processors (AMPs) with limited power budget. A modelling and task
scheduling/allocation is proposed in order to efficiently make use of the
existing features on big.LITTLE ARM processors, including: (I) source-code
adaptation for parallel computing, which enables code acceleration by applying
the OmpSs programming model, a task-based programming model that handles
data-dependencies between tasks in a transparent fashion; (II) different OmpSs
task allocation policies which take into account the processor asymmetry and
can dynamically set processing resources in a more efficient way based on their
particular features. The proposed mechanism can be efficiently applied to take
advantage of the processing elements existing on low-cost and low-energy
multi-core embedded devices executing object detection algorithms based on
cascading classifiers. Although these classifiers yield the best results for
detection algorithms in the field of computer vision, their high computational
requirements prevent them from being used on these devices under real-time
requirements. Finally, we compare the energy efficiency of a heterogeneous
architecture based on asymmetric multicore processors with a suitable task
scheduling, with that of a homogeneous symmetric architecture
Parallelized Seeded Region Growing Using CUDA
This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests