Face parallel identification method based on deep learning and Spark

A technology of deep learning and face recognition, which is applied in the field of parallel recognition of face recognition, can solve problems such as low recognition rate, poor real-time performance, and long training time of classifier models, so as to improve training, improve overall speed, and improve classification The effect of recognition accuracy

Inactive Publication Date: 2018-06-29
田文洪 +5
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Problems solved by technology

[0004] In view of the poor real-time performance and low recognition rate of most current face recognition algorithms, the reasons are roughly as follows: 1. The training time of the classifier model in the process of face recognition is too long; 2. The classification effect of the classifier is not good

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  • Face parallel identification method based on deep learning and Spark
  • Face parallel identification method based on deep learning and Spark

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[0015] Below, according to the accompanying drawings and the content of the invention, the specific embodiments of the present invention will be further described in detail. The following examples are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0016] Step 1: First, divide the extracted facial features into blocks and then process and store them on the HDFS file system, input them into Spark, and convert them into Blocks;

[0017] Step 2: After the Spark data input forms an RDD, in the Transition stage, the TensorFlow framework is used on each node, and the deep learning classifier model trained by TensorFlow is called in the framework to process the features and achieve the purpose of classification. The deep learning classifier model is trained by a convolutional neural network. The convolutional neural network structure is: convolutional layer, downsampling layer, and full connection layer;

[0018] Step 3: The j...

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Abstract

The invention discloses a face parallel identification method based on deep learning and Spark, and via the method, face identification is realized via a deep learning framework and Spark parallelization. In the image feature extraction process, a graph is distributed to different cluster nodes via Partition, each node of a Spark cluster is used to train parameters and models in parallel, one nodeis generated in each cluster node, the models are used for parallel face identification later, and less time is consumed via parallelization. In the face identification process, Spark characteristicsare used, the characteristic is broadcast to each cluster node in a parallel identification manner, calculation is carried out in each node, and each node generates a corresponding result of the faceto be identified. The results of all sub nodes are gathered in a main node, and are compared to obtain a final result.

Description

technical field [0001] The invention belongs to the field of face recognition methods and recognition, in particular to a parallel recognition method for face recognition. Background technique [0002] With the construction of smart cities in my country, face recognition has become a rapidly developing field second only to fingerprint recognition. Face recognition is based on the extracted face image features using correlation recognition algorithms for face confirmation or identification. The detected face to be recognized is compared and matched with the known face in the database to obtain relevant information. The key to this process is to select an appropriate face representation method and matching strategy. The system structure is closely related to the face representation method. relevant. Generally, different recognition algorithms are selected for measurement according to the proposed features, and commonly used methods include distance measurement, support vecto...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06F18/241
Inventor 田文洪任小芹刘弘一黄文强黄超杰何马均
Owner 田文洪
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