Face Detection Using HMM –SVM Method

Rajput, Nupur; Jain, Pranita; Shrivastava, Shailendra Kumar

doi:10.1007/978-3-642-30111-7_80

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Convolutional neural network can achieve more than two orders of magnitude improvement compared to the human identification accuracy [5,6]. With its powerful learning ability, convolutional neural network has been widely used in target tracking [7,8], face detection [9][10][11], license plate detection [12], and handwriting recognition [13,14]. It is an important research topic in machine learning, computer vision, mobile information system, and other scientific research fields.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

Zhang¹,

Xiao²,

Wan³

et al. 2017

Mobile Information Systems

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With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

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