# Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

Authors: Ming Liu; Renzheng Xue; Xiaoye Li
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In order to solve the practical problem that the massive video data generated by monitoring equipment cannot be processed efficiently temporarily, this paper proposes a framework for face recognition of massive video data based on distributed environment. Combined with the application features of face recognition, this method designs a strategy for fast reading of massive video data and optimizes the feature data obtained by the cloud platform, so as to speed up the retrieval speed of face features. The results are as follows: the compression rate of the proposed compression method is higher than that of the traditional matrix triple and binary methods, which is increased by about 65%; the data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than that in the nonoptimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original; the experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. It is proved that this framework has certain research significance in dealing with massive unstructured data. It not only provides theoretical reference value for the research of massive video processing but also makes a contribution to the actual industry.


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## Body

## 1. Introduction

With the development of the Internet and the doubling of Internet data, the Internet has entered the era of big data. The four characteristics of big data can be summarized as “4V” : Massive data scale (VAST), fast data flow (Velocity), rich data types (Variety), and huge data value (Value). In terms of data scale alone, it took about one year for the global Internet to generate data of 1ep in 2001, one day in 2013, and only half a day in 2016. Moreover, the world produces new data at a growth rate of 40% every year. So far, the total amount of global information can double every two years. In 2012, the total amount of global data has reached 1.87 zb, and this number will double every two years [1]. By 2020, the total amount of data in the world will reach 35 to 40 zb.Big data not only refers to massive data but also has a variety of structures, including all kinds of structured data and unstructured data (such as video files, audio files, and pictures). In addition, the massive data may also include missing data and incomplete data, which pose severe challenges to data processing [2].The emergence of big data technology makes it possible to quickly process massive data. Effectively mining the information in big data will greatly promote the development of society. In practical application, social security or public security criminal investigation often need to obtain face information from video. In the face of massive video data, it has become impossible to manually find or borrow the processing capacity of a single machine. With the help of big data computing framework deployed on cheap machine cluster or cloud platform, face recognition of massive video can effectively reduce the labor cost of traditional methods. This framework for fast processing massive video needs to be highly scalable, fast processing massive data, and timely responding to user needs [3].


## 2. Literature Review

On the one hand, with the rapid growth of Internet data, relying on high-performance computing to complete data processing has encountered a bottleneck. The reason is that high-performance computers are confined to the existing hardware technology. With the improvement of processing speed, the cost for performance improvement research will also increase significantly, but the performance improvement is often not very significant. The traditional way of processing massive data cannot meet the explosive demand of the Internet. On the other hand, since 2003, big data processing platforms for massive data have been put forward continuously. So far, there has been a very rich ecosystem of big data processing technology. This provides another idea for the processing of big data to improve the computing power vertically. By expanding the ordinary PC horizontally, the processing of massive data can be completed [4]. In recent years, with the continuous development of cloud computing technology, cloud computing technology is known as the third wave of science and technology after personal computer technology and Internet technology. Cloud computing integrates heterogeneous computing resources, virtualizes computing resources, and provides users with an on-demand charging mode. Cloud computing technology greatly improves the efficiency of dynamic management of computing resources in big data services. Many Internet companies in the world are also popular with cloud computing technology and provide convenient PAAS services or SaaS services. The combination of big data processing platform and cloud computing technology makes big data processing technology more convenient and easy to use in terms of computing resources such as network, storage, and CPU, which also leads to the research frenzy of international companies and research institutions in the fields related to cloud computing technology and big data, which also greatly promotes the rapid development of big data and finally forms a complete and rich big data ecological environment [5].It has been ten years since Google put forward the concept of cloud computing in 2006. Cloud computing plays an important role in big data processing. Similarly, from the release of distributed file system GFS and distributed processing frame MapReduce by Google in 2003 to the big data processing platform, after more than ten years of accumulation, big data processing technology has made great strides. Today's big data ecosystem has developed components of various computing services centered on Hadoop. The whole big data ecosystem has covered all aspects of big data application [6].Based on the current research, this paper designs a framework for face recognition of massive video data. Combined with the characteristics of face recognition task, a reading model for unstructured data such as video is proposed, and the face feature data is optimized while extracting face features, which makes the distributed computing platform process massive video with high scalability and rapidity, and provides a feasible framework for the application of massive video face recognition. Through the implementation and testing on spark platform, the feasibility of the framework proposed in this paper is proved. The framework in this paper is suitable for the task of finding a target character from massive videos in the context of big data. It provides an idea for distributed computing platform to read massive videos, and provides an optimization method of face image feature data in the context of big data [7].


## 3. Research Methods

### 3.1. Distributed Computing Platform

After Google put forward GFS and MapReduce technology, it implemented it and opened source to form today's Hadoop ecosystem components. The initial opensource projects were HDFS and MapReduce as distributed file system and distributed computing framework, respectively. On this basis, many rich big data applications have been established. Hadoop distributed system is mainly divided into two parts: HDFS distributed file system and MapReduce computing framework [8].


#### 3.1.1. HDFS Distributed File System

The function of distributed file system is to transfer file information to other nodes through sharing among multiple servers. This method is very important for distributed system. Moreover, in terms of function, the distributed file system should allow users to access file services. On this basis, it should ensure that the quality of service of the whole system can approach or exceed the performance of the local file system. The distributed file system is implemented on the basis of cloud computing and provides users with remote file system services. However, this design should also ensure the transparency of the distributed file system, so that users cannot feel the existence of the distributed file system, and cannot be inferior to the local file system in operation and service quality. This transparency is a key, which is directly related to the user experience. In addition, in the implementation of the distributed file system, the distributed file system should ensure its high availability, that is, to provide users with a convenient, available, and secure file system. At the same time, the distributed file system should consider more problems than the local file system, such as the support of concurrent access to the file system and the consistency of multiple copies, writes, and modifications of data [9].The system architecture of HDFS is a typical master-slave framework. The architecture diagram of HDFS file system is shown in Figure1. The whole HDFS cluster consists of one NameNode, one SecondaryNameNode, and several DataNode. NameNode is the central control node, which is responsible for the management of files in the whole file system. The NameNode responds to the file access request sent by the client and returns the file storage location on the NameNode to the client. The NameNode controls the DataNode through the control command on the management file system. The DataNode sends the status information on the local node to the NameNode through the heartbeat mechanism. When the heartbeat stops, the NameNode will deal with the stopped node accordingly [10]. In this master-slave mode system, the shutdown of the master node will cause the collapse of the whole system. Therefore, the SecondaryNameNode backs up the data on the NameNode. When the NameNode fails and stops working, the SecondaryNameNode can use the previous backup data to provide services for the slave node.Figure 1
Framework of HDFS file system.


#### 3.1.2. MapReduce Computing Model

MapReduce is an important basic computing model in the Hadoop system. Other plugins in Hadoop ecology usually take MapReduce as the basic framework. When processing massive datasets, MapReduce abstracts the whole calculation process into two processes: map and reduce. Map processes the datasets of K/V key value pairs, and reduce regulates the processing results [11].When implementing MapReduce programming, mapper function and reducer function need to be implemented. Mapper function is to process the input K/V key value pair. When dealing with key value pairs, MapReduce does not require too many input and output types of K/V. MapReduce provides many implementation interfaces. Users can customize different data K/V types according to their own needs. Mapper function is to process the data fragments generated when the MapReduce framework calls jobs. Each mapper corresponds to a data fragment, and the output of map does not require one-to-one correspondence with the input. The job execution process of MapReduce is shown in Figure2.Figure 2
MapReduce job execution flowchart.


### 3.2. Spark Distributed Computing System

Spark was born in AMPLab at the University of California, Berkeley, in 2009. At present, spark has become a project level opensource project under the Apache Software Foundation [12]. Spark can be regarded as an alternative to MapReduce. It can be compatible with HDFS and hive distributed storage layers, and can be integrated into Hadoop ecosystem to make up for the shortcomings of MapReduce.Spark is a big data parallel computing framework based on memory computing. Based on memory computing, spark improves the real-time performance of data processing in the big data environment, ensures high fault tolerance and high scalability, and allows users to deploy spark on a large number of cheap hardware to form a cluster [13]. Spark's architecture also adopts the master-slave model in distributed computing. Master is the node running the master process in the cluster, and slave is the node running the worker process in the cluster. The role of master is the same as that of JobTracker in MapReduce, which controls the operation of the whole cluster and is responsible for the normal operation of the whole cluster; the worker is the same as the TaskTracker in MapReduce. As a computing node in the cluster, it receives commands from the master node and reports the status of its own node to the master node. The executor is responsible for task execution: the client is mainly used as a client for users to submit applications, while the driver controls the execution of the whole application and prints the log of control results. The architecture of spark is shown in Figure 3.Figure 3
Spark structure frame.


### 3.3. Machine Learning Face Recognition Method

There are many kinds of traditional machine learning methods for face recognition: knowledge-based method, feature invariant method, template matching method, and appearance-based method. This paper mainly deals with the massive video carried out by spark without too much discussion on the technology of face recognition [14].The Eigenface method reduces the dimension of the pixel matrix through PCA and finally determines whether it is the same person by calculating the distance of pixels in the low dimensional space. The algorithm is described as follows:LetX=x1,x2,…,xn represent a random feature, where xi∈Rd.(1)
First, calculate the mean vectorμ, as shown in formula (1):(1)μ=1n∑i=1nxi.(2)
Calculate the covariance matrix S, as shown in formula (2):(2)S=1n∑i=1nxi−μxi−μT.(3)
Calculate the eigenvalue of S and the corresponding eigenvector, as shown in equation (3):(3)Svi=λivi,i=1,2,…,n.(4)
The eigenvalues and their corresponding eigenvectors are sorted in descending order, and the K principal components are the eigenvectors corresponding to the largest feature. The K principal components y of X are shown in formula (4):(4)y=WTx−μ,W=v1,v2,…,vk.(5)
Reconstruct featurex, as shown in equation (5):(5)x=Wy+μ.After PCA dimensionality reduction, the feature quantity encodes the face features and illumination.


### 3.4. Massive Video Face Recognition Technology

A video processing method implemented on HBase is proposed by scholars. The video is transformed into video frame image, which is processed by the coprocessor of HBase. Coprocessor is similar to a database trigger, which triggers the action when operating the table. The data table, face table, and keypoint table are designed on HBase to store the image, the extracted face, and face features, respectively. When processing video frame images, the face detection and feature extraction are completed in coprocessor, and the matching of face features by HBase is used to find the face in a specific image [15]. However, because the extracted frame video pictures are extracted from continuous frames, even if the contents of the pictures are not different, they are processed, which produces redundant information. The processing is not only time consuming, but also the amount of data information is often larger than the amount of original video data. This paper proposes a method to extract the key frames of video pictures on the basis of solving the problem of increasing the amount of data after video decompression [16].Therefore, in the implementation of massive video face recognition framework, the face recognition data processing scene is different from the application of massive video transcoding. The data segmentation granularity of video data transcoding is larger than that of video face processing. The method used for video transcoding is not suitable for massive video face recognition [17]. In the aspect of video data reading, the smaller granularity of video data processing can transform video processing into picture processing.The process of massive video face and feature extraction is shown in Figure4. This framework includes a distributed file system for storing massive video files, a distributed computing framework for processing massive video, and a distributed database for storing processing results. The distributed database has the ability to store massive data and provides reliable security. For example, in the implementation part of this paper, massive video data is stored in HDFS, and HDFS completes the fault-tolerant storage of data. The default number of backups of HDFS is 3. In the cloud computing environment, HDFS ensures the fault-tolerant and disaster tolerant ability of data by storing the same data in any server, servers in the same rack, and servers in different racks [18].Figure 4
Distributed massive video face recognition framework.


## 3.1. Distributed Computing Platform

After Google put forward GFS and MapReduce technology, it implemented it and opened source to form today's Hadoop ecosystem components. The initial opensource projects were HDFS and MapReduce as distributed file system and distributed computing framework, respectively. On this basis, many rich big data applications have been established. Hadoop distributed system is mainly divided into two parts: HDFS distributed file system and MapReduce computing framework [8].


### 3.1.1. HDFS Distributed File System

The function of distributed file system is to transfer file information to other nodes through sharing among multiple servers. This method is very important for distributed system. Moreover, in terms of function, the distributed file system should allow users to access file services. On this basis, it should ensure that the quality of service of the whole system can approach or exceed the performance of the local file system. The distributed file system is implemented on the basis of cloud computing and provides users with remote file system services. However, this design should also ensure the transparency of the distributed file system, so that users cannot feel the existence of the distributed file system, and cannot be inferior to the local file system in operation and service quality. This transparency is a key, which is directly related to the user experience. In addition, in the implementation of the distributed file system, the distributed file system should ensure its high availability, that is, to provide users with a convenient, available, and secure file system. At the same time, the distributed file system should consider more problems than the local file system, such as the support of concurrent access to the file system and the consistency of multiple copies, writes, and modifications of data [9].The system architecture of HDFS is a typical master-slave framework. The architecture diagram of HDFS file system is shown in Figure1. The whole HDFS cluster consists of one NameNode, one SecondaryNameNode, and several DataNode. NameNode is the central control node, which is responsible for the management of files in the whole file system. The NameNode responds to the file access request sent by the client and returns the file storage location on the NameNode to the client. The NameNode controls the DataNode through the control command on the management file system. The DataNode sends the status information on the local node to the NameNode through the heartbeat mechanism. When the heartbeat stops, the NameNode will deal with the stopped node accordingly [10]. In this master-slave mode system, the shutdown of the master node will cause the collapse of the whole system. Therefore, the SecondaryNameNode backs up the data on the NameNode. When the NameNode fails and stops working, the SecondaryNameNode can use the previous backup data to provide services for the slave node.Figure 1
Framework of HDFS file system.


### 3.1.2. MapReduce Computing Model

MapReduce is an important basic computing model in the Hadoop system. Other plugins in Hadoop ecology usually take MapReduce as the basic framework. When processing massive datasets, MapReduce abstracts the whole calculation process into two processes: map and reduce. Map processes the datasets of K/V key value pairs, and reduce regulates the processing results [11].When implementing MapReduce programming, mapper function and reducer function need to be implemented. Mapper function is to process the input K/V key value pair. When dealing with key value pairs, MapReduce does not require too many input and output types of K/V. MapReduce provides many implementation interfaces. Users can customize different data K/V types according to their own needs. Mapper function is to process the data fragments generated when the MapReduce framework calls jobs. Each mapper corresponds to a data fragment, and the output of map does not require one-to-one correspondence with the input. The job execution process of MapReduce is shown in Figure2.Figure 2
MapReduce job execution flowchart.


## 3.1.1. HDFS Distributed File System

The function of distributed file system is to transfer file information to other nodes through sharing among multiple servers. This method is very important for distributed system. Moreover, in terms of function, the distributed file system should allow users to access file services. On this basis, it should ensure that the quality of service of the whole system can approach or exceed the performance of the local file system. The distributed file system is implemented on the basis of cloud computing and provides users with remote file system services. However, this design should also ensure the transparency of the distributed file system, so that users cannot feel the existence of the distributed file system, and cannot be inferior to the local file system in operation and service quality. This transparency is a key, which is directly related to the user experience. In addition, in the implementation of the distributed file system, the distributed file system should ensure its high availability, that is, to provide users with a convenient, available, and secure file system. At the same time, the distributed file system should consider more problems than the local file system, such as the support of concurrent access to the file system and the consistency of multiple copies, writes, and modifications of data [9].The system architecture of HDFS is a typical master-slave framework. The architecture diagram of HDFS file system is shown in Figure1. The whole HDFS cluster consists of one NameNode, one SecondaryNameNode, and several DataNode. NameNode is the central control node, which is responsible for the management of files in the whole file system. The NameNode responds to the file access request sent by the client and returns the file storage location on the NameNode to the client. The NameNode controls the DataNode through the control command on the management file system. The DataNode sends the status information on the local node to the NameNode through the heartbeat mechanism. When the heartbeat stops, the NameNode will deal with the stopped node accordingly [10]. In this master-slave mode system, the shutdown of the master node will cause the collapse of the whole system. Therefore, the SecondaryNameNode backs up the data on the NameNode. When the NameNode fails and stops working, the SecondaryNameNode can use the previous backup data to provide services for the slave node.Figure 1
Framework of HDFS file system.


## 3.1.2. MapReduce Computing Model

MapReduce is an important basic computing model in the Hadoop system. Other plugins in Hadoop ecology usually take MapReduce as the basic framework. When processing massive datasets, MapReduce abstracts the whole calculation process into two processes: map and reduce. Map processes the datasets of K/V key value pairs, and reduce regulates the processing results [11].When implementing MapReduce programming, mapper function and reducer function need to be implemented. Mapper function is to process the input K/V key value pair. When dealing with key value pairs, MapReduce does not require too many input and output types of K/V. MapReduce provides many implementation interfaces. Users can customize different data K/V types according to their own needs. Mapper function is to process the data fragments generated when the MapReduce framework calls jobs. Each mapper corresponds to a data fragment, and the output of map does not require one-to-one correspondence with the input. The job execution process of MapReduce is shown in Figure2.Figure 2
MapReduce job execution flowchart.


## 3.2. Spark Distributed Computing System

Spark was born in AMPLab at the University of California, Berkeley, in 2009. At present, spark has become a project level opensource project under the Apache Software Foundation [12]. Spark can be regarded as an alternative to MapReduce. It can be compatible with HDFS and hive distributed storage layers, and can be integrated into Hadoop ecosystem to make up for the shortcomings of MapReduce.Spark is a big data parallel computing framework based on memory computing. Based on memory computing, spark improves the real-time performance of data processing in the big data environment, ensures high fault tolerance and high scalability, and allows users to deploy spark on a large number of cheap hardware to form a cluster [13]. Spark's architecture also adopts the master-slave model in distributed computing. Master is the node running the master process in the cluster, and slave is the node running the worker process in the cluster. The role of master is the same as that of JobTracker in MapReduce, which controls the operation of the whole cluster and is responsible for the normal operation of the whole cluster; the worker is the same as the TaskTracker in MapReduce. As a computing node in the cluster, it receives commands from the master node and reports the status of its own node to the master node. The executor is responsible for task execution: the client is mainly used as a client for users to submit applications, while the driver controls the execution of the whole application and prints the log of control results. The architecture of spark is shown in Figure 3.Figure 3
Spark structure frame.


## 3.3. Machine Learning Face Recognition Method

There are many kinds of traditional machine learning methods for face recognition: knowledge-based method, feature invariant method, template matching method, and appearance-based method. This paper mainly deals with the massive video carried out by spark without too much discussion on the technology of face recognition [14].The Eigenface method reduces the dimension of the pixel matrix through PCA and finally determines whether it is the same person by calculating the distance of pixels in the low dimensional space. The algorithm is described as follows:LetX=x1,x2,…,xn represent a random feature, where xi∈Rd.(1)
First, calculate the mean vectorμ, as shown in formula (1):(1)μ=1n∑i=1nxi.(2)
Calculate the covariance matrix S, as shown in formula (2):(2)S=1n∑i=1nxi−μxi−μT.(3)
Calculate the eigenvalue of S and the corresponding eigenvector, as shown in equation (3):(3)Svi=λivi,i=1,2,…,n.(4)
The eigenvalues and their corresponding eigenvectors are sorted in descending order, and the K principal components are the eigenvectors corresponding to the largest feature. The K principal components y of X are shown in formula (4):(4)y=WTx−μ,W=v1,v2,…,vk.(5)
Reconstruct featurex, as shown in equation (5):(5)x=Wy+μ.After PCA dimensionality reduction, the feature quantity encodes the face features and illumination.


## 3.4. Massive Video Face Recognition Technology

A video processing method implemented on HBase is proposed by scholars. The video is transformed into video frame image, which is processed by the coprocessor of HBase. Coprocessor is similar to a database trigger, which triggers the action when operating the table. The data table, face table, and keypoint table are designed on HBase to store the image, the extracted face, and face features, respectively. When processing video frame images, the face detection and feature extraction are completed in coprocessor, and the matching of face features by HBase is used to find the face in a specific image [15]. However, because the extracted frame video pictures are extracted from continuous frames, even if the contents of the pictures are not different, they are processed, which produces redundant information. The processing is not only time consuming, but also the amount of data information is often larger than the amount of original video data. This paper proposes a method to extract the key frames of video pictures on the basis of solving the problem of increasing the amount of data after video decompression [16].Therefore, in the implementation of massive video face recognition framework, the face recognition data processing scene is different from the application of massive video transcoding. The data segmentation granularity of video data transcoding is larger than that of video face processing. The method used for video transcoding is not suitable for massive video face recognition [17]. In the aspect of video data reading, the smaller granularity of video data processing can transform video processing into picture processing.The process of massive video face and feature extraction is shown in Figure4. This framework includes a distributed file system for storing massive video files, a distributed computing framework for processing massive video, and a distributed database for storing processing results. The distributed database has the ability to store massive data and provides reliable security. For example, in the implementation part of this paper, massive video data is stored in HDFS, and HDFS completes the fault-tolerant storage of data. The default number of backups of HDFS is 3. In the cloud computing environment, HDFS ensures the fault-tolerant and disaster tolerant ability of data by storing the same data in any server, servers in the same rack, and servers in different racks [18].Figure 4
Distributed massive video face recognition framework.


## 4. Result Analysis

### 4.1. Comparison of Algorithm Compression

The two designed feature extraction and optimization methods are tested, respectively. Firstly, the proposed compression method is compared with the traditional triple and binary methods [19]. In this paper, a total of 82,060 face features are extracted, and the amount of data achieved by the three methods is compared. However, because the implementation of the algorithm may be different, the running time is not compared is shown in Figure 5 [20].Figure 5
Comparison of data volume of different compression methods.After comparison, it is found that the compression rate of the compression method proposed in this paper is higher than that of the traditional matrix triple and binary methods.


### 4.2. Comparison of Operation Time

In order to test whether the optimization of LBP feature vector in this paper reduces the execution time of the framework, this paper carries out compression and non compression experiments on 144,880 face feature data obtained from 200 GB video data processing [21]. The experimental platform consists of 10 Intel-i3-4130 quad core processors, 8 GB memory, Ubuntu14.01 operating system, one master node, and nine slave nodes, as shown in Table 1.Table 1
Results of compressed and uncompressed characteristic data.
CompressedUncompressedProportionCharacteristic data volume1269 MB8996 MB1 : 7.08Retrieval time2.73 s12.60 s1 : 4.62Feature processing time2.31 s1.56 s1.48 : 1The experimental results are shown in Table1. The data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than the data in the non optimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original. Experiments show that the data optimization method in this paper greatly reduces the amount of feature data and the total running time of the framework [22].


### 4.3. Extensibility Verification of Spark Face Recognition for Massive Video Faces

In order to verify the scalability of the framework for processing video data, the system sets the worker nodes to 1, 3, 5, 7, and 9, respectively, in Spark's standalone mode. The scalability of video processing in this framework is tested by measuring the time taken to process 200 GB data [23]. The experimental results are shown in Figure 6.Figure 6
Scalability of spark face recognition for massive video faces.The experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. Therefore, with the increase of processing nodes, the acceleration ratio of the system basically increases linearly, the total running time of the system increases less, and the system has good scalability [24, 25].


## 4.1. Comparison of Algorithm Compression

The two designed feature extraction and optimization methods are tested, respectively. Firstly, the proposed compression method is compared with the traditional triple and binary methods [19]. In this paper, a total of 82,060 face features are extracted, and the amount of data achieved by the three methods is compared. However, because the implementation of the algorithm may be different, the running time is not compared is shown in Figure 5 [20].Figure 5
Comparison of data volume of different compression methods.After comparison, it is found that the compression rate of the compression method proposed in this paper is higher than that of the traditional matrix triple and binary methods.


## 4.2. Comparison of Operation Time

In order to test whether the optimization of LBP feature vector in this paper reduces the execution time of the framework, this paper carries out compression and non compression experiments on 144,880 face feature data obtained from 200 GB video data processing [21]. The experimental platform consists of 10 Intel-i3-4130 quad core processors, 8 GB memory, Ubuntu14.01 operating system, one master node, and nine slave nodes, as shown in Table 1.Table 1
Results of compressed and uncompressed characteristic data.
CompressedUncompressedProportionCharacteristic data volume1269 MB8996 MB1 : 7.08Retrieval time2.73 s12.60 s1 : 4.62Feature processing time2.31 s1.56 s1.48 : 1The experimental results are shown in Table1. The data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than the data in the non optimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original. Experiments show that the data optimization method in this paper greatly reduces the amount of feature data and the total running time of the framework [22].


## 4.3. Extensibility Verification of Spark Face Recognition for Massive Video Faces

In order to verify the scalability of the framework for processing video data, the system sets the worker nodes to 1, 3, 5, 7, and 9, respectively, in Spark's standalone mode. The scalability of video processing in this framework is tested by measuring the time taken to process 200 GB data [23]. The experimental results are shown in Figure 6.Figure 6
Scalability of spark face recognition for massive video faces.The experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. Therefore, with the increase of processing nodes, the acceleration ratio of the system basically increases linearly, the total running time of the system increases less, and the system has good scalability [24, 25].


## 5. Conclusion

With the extensive application of monitoring equipment in the field of social security, the video data generated by monitoring equipment gradually exceed the traditional storage, management, and analysis ability. The massive video data generated by these equipment is of great significance to social security and public security criminal investigation. At the same time, it also has high requirements for the timeliness of these data processing. Since entering the new century, with the continuous development of computer technology, cloud computing technology has been applied. This provides convenient and easy-to-use computing, storage, network, and other resources for massive data processing. With the development of the times and the popularity of intelligent devices, massive big data is in urgent need of an effective method for processing. The massive video data processing technology developed on the basis of cloud computing is applied. The big data processing platform represented by Hadoop Spark provides a technology that integrates storage, processing, query, and other general functions for massive data. These technologies not only have the computing power to deal with massive video but also have a good guarantee in aspects not involved in traditional technologies such as data fault tolerance. This paper designs a framework for face recognition of massive video data in a distributed environment, which provides an application that can query the target characters in massive surveillance video in a short time for the fields of public security and criminal investigation.



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*Source: 2802901-2022-07-22.xml*