CN105488859A - Work attendance system based on face identification and voice recognition - Google Patents

Work attendance system based on face identification and voice recognition Download PDF

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Publication number
CN105488859A
CN105488859A CN201510822481.9A CN201510822481A CN105488859A CN 105488859 A CN105488859 A CN 105488859A CN 201510822481 A CN201510822481 A CN 201510822481A CN 105488859 A CN105488859 A CN 105488859A
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voice
recognition
face
module
gabor
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夏钢
夏泽宇
陈牧遥
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Suzhou Ming Crown Software Technology Co Ltd
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Suzhou Ming Crown Software Technology Co Ltd
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
    • G07C1/10—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people together with the recording, indicating or registering of other data, e.g. of signs of identity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Collating Specific Patterns (AREA)

Abstract

The invention discloses a work attendance system based on face identification and voice recognition. The work attendance system comprises a face image acquisition module, a face image preprocessing module, a voice recording module, a voice preprocessing module, a sample database, a face identification module, a voice recognition module, a work attendance database and a voice broadcasting module which is used for broadcasting success in work attendance when results of the face identification module and the voice recognition module have both been identified and for broadcasting failure in work attendance when the results are not identified.

Description

Attendance system based on face recognition and voice recognition
Technical Field
The invention relates to the technical field of face detection, in particular to an attendance system based on face recognition and voice recognition.
Background
The identification problem is a difficult problem which is often encountered in daily production and life of human beings, and is an important prerequisite for guaranteeing the production and life in many cases. The traditional identification method is mainly based on identification articles and identification knowledge, and the two identifications can be regarded as 'owned by a principal' and 'known by the principal' respectively. The former mainly comprises keys, certificates, cards and the like, and people holding the identity identification articles can obtain identity authentication in public and private places; the latter mainly includes password, user name, prompting question and answer, etc., and anyone can obtain the identity of the identified main body by knowing the information. However, in some cases where the security requirements in practical applications are high, people usually combine the identification articles and identification knowledge, for example, in an Automatic Teller Machine (ATM), the withdrawal needs to be completed by holding a bank card and a password at the same time. With the development of computer science and technology and the popularization of networks, some information of people becomes safer, such as identity document numbers, passwords and the like, so that the current society puts higher requirements on the accuracy, safety and practicability of human identity recognition. Conventional identification methods face serious challenges. The identity identification article is easy to forge, lose or be damaged, identity identification knowledge is easy to forget or steal, and the traditional identity identification is more and more not suitable for the modern of scientific and technical high-speed development.
The face detection technology, as one of the biometric technologies, has the following advantages: (1) safety, every person in the world has a unique face different from others, and even the facial features of twin brothers and sisters are slightly different; (2) the method has the advantages that the method is easy to obtain, the face image can be collected by using simple camera equipment, the collected person does not need to be contacted during collection, and most of clients can accept the method; (3) the identity of the main body is identified more realistically because the main body is never separated; (4) the expression information obtained by face detection cannot be obtained by other biological detection techniques. Therefore, the face detection technology provides an excellent solution for modern identity recognition.
Disclosure of Invention
The invention aims to solve the technical problem of providing an attendance system based on face recognition and voice recognition, which is more accurate in attendance.
In order to solve the technical problem, the invention provides an attendance system based on face recognition and voice recognition, which comprises:
the face image acquisition module is used for acquiring a face image;
the face image preprocessing module is used for processing the face image acquired by the face image acquisition module to acquire a current low-dimensional Gabor characteristic image;
the voice recording module is used for recording voice;
the voice preprocessing module is used for carrying out noise reduction processing on the voice recorded by the voice recording module to obtain the current low-noise voice;
the sample database is used for storing a plurality of low-dimensional Gabor characteristic image samples and low-noise voice samples which are recorded in advance;
the face recognition module is used for comparing the current low-dimensional Gabor characteristic image with the plurality of low-dimensional Gabor characteristic image samples one by adopting a nearest neighbor recognition method, and judging that the recognition result is not recognized when the distances between all the low-dimensional Gabor characteristic image samples in the database and the current low-dimensional Gabor characteristic image are greater than a preset threshold value, or judging that the recognition result is recognized;
the voice recognition module is used for comparing the current low-noise voice with the low-noise voice samples one by one, and when the distances between all the low-noise voice samples in the database and the current low-noise voice are larger than a preset threshold value, judging that the recognition result is not recognized, otherwise, judging that the recognition result is recognized;
the attendance database is used for recording attendance time when the results of the face recognition module and the voice recognition module are recognized;
and the voice broadcasting module is used for broadcasting the attendance successfully when the results of the face recognition module and the voice recognition module are recognized, or else, broadcasting the attendance failure.
The implementation of the invention has the following beneficial effects: according to the invention, the Gabor filter is adopted to judge the acquired Gabor characteristic image, so that the detection result is more accurate, and the attendance is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system block diagram of an embodiment of an attendance system based on face recognition and voice recognition provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a system block diagram of an embodiment of an attendance system based on face recognition and voice recognition, as shown in fig. 1, including:
the face image acquisition module is used for acquiring a face image; in specific implementation, the face image acquisition module can be a camera;
the face image preprocessing module is used for processing the face image acquired by the face image acquisition module to acquire a current low-dimensional Gabor characteristic image;
the voice recording module is used for recording voice;
the voice preprocessing module is used for carrying out noise reduction processing on the voice recorded by the voice recording module to obtain the current low-noise voice;
the sample database is used for storing a plurality of low-dimensional Gabor characteristic image samples and low-noise voice samples which are recorded in advance;
the face recognition module is used for comparing the current low-dimensional Gabor characteristic image with the plurality of low-dimensional Gabor characteristic image samples one by adopting a nearest neighbor recognition method, and judging that the recognition result is not recognized when the distances between all the low-dimensional Gabor characteristic image samples in the database and the current low-dimensional Gabor characteristic image are greater than a preset threshold value, or judging that the recognition result is recognized;
the voice recognition module is used for comparing the current low-noise voice with the low-noise voice samples one by one, and when the distances between all the low-noise voice samples in the database and the current low-noise voice are larger than a preset threshold value, judging that the recognition result is not recognized, otherwise, judging that the recognition result is recognized;
the attendance database is used for recording attendance time when the results of the face recognition module and the voice recognition module are recognized;
and the voice broadcasting module is used for broadcasting the attendance successfully when the results of the face recognition module and the voice recognition module are recognized, or else, broadcasting the attendance failure.
The face preprocessing module specifically comprises:
the graying unit is used for graying the color face image acquired by the face image acquisition module to obtain a gray face image;
the Gabor feature extraction unit is used for processing the gray face image by adopting a Gabor filter to obtain a Gabor feature image;
and the dimension reduction unit is used for reducing the dimension of the Gabor characteristic image by adopting a principal component analysis method to obtain a low-dimensional Gabor characteristic image.
Wherein the Gabor feature extraction unit is specifically used for adopting a Gabor filter to carry out gray level face imageProcessing to obtain Gabor characteristic imageWherein,representing convolution operation for gray face image pixel,is a function of a Gabor filter, anIn the formula, mu and nu respectively represent the direction and the scale of the Gabor kernel, | | | | represents norm, k → μ , ν = k ν e jφ μ , k ν = k max f ν , φ μ = π μ N φ , σ＝2π， k m a x = π 2 , f ν = 2 , Nφ8. The orientation μ of the Gabor nucleus is 8. The Gabor core has a dimension v of 5.
Wherein the dimension reduction unit is specifically configured to:
according to the optimization problem: solving a group of projection matrixes V to maximize the total scattering of matrixes projected on the Gabor characteristic image, and establishing an objective function J (V) -maxTr (S)V) (ii) a Wherein S isVCovariance matrix, Tr (S), representing Gabor feature imageV) Denotes SVThe trace of (2);
calculating to obtain a projection matrix V according to the optimization problem;
and performing projection dimensionality reduction on the Gabor characteristic image according to the projection matrix V to obtain a low-dimensional Gabor characteristic image.
The implementation of the invention has the following beneficial effects: according to the invention, the Gabor filter is adopted to judge the acquired Gabor characteristic image, so that the detection result is more accurate, and the attendance is more accurate.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. The utility model provides an attendance system based on face identification and speech recognition which characterized in that includes:
the face image acquisition module is used for acquiring a face image;
the face image preprocessing module is used for processing the face image acquired by the face image acquisition module to acquire a current low-dimensional Gabor characteristic image;
the voice recording module is used for recording voice;
the voice preprocessing module is used for carrying out noise reduction processing on the voice recorded by the voice recording module to obtain the current low-noise voice;
the sample database is used for storing a plurality of low-dimensional Gabor characteristic image samples and low-noise voice samples which are recorded in advance;
the face recognition module is used for comparing the current low-dimensional Gabor characteristic image with the plurality of low-dimensional Gabor characteristic image samples one by adopting a nearest neighbor recognition method, and judging that the recognition result is not recognized when the distances between all the low-dimensional Gabor characteristic image samples in the database and the current low-dimensional Gabor characteristic image are greater than a preset threshold value, or judging that the recognition result is recognized;
the voice recognition module is used for comparing the current low-noise voice with the low-noise voice samples one by one, and when the distances between all the low-noise voice samples in the database and the current low-noise voice are larger than a preset threshold value, judging that the recognition result is not recognized, otherwise, judging that the recognition result is recognized;
the attendance database is used for recording attendance time when the results of the face recognition module and the voice recognition module are recognized;
and the voice broadcasting module is used for broadcasting the attendance successfully when the results of the face recognition module and the voice recognition module are recognized, or else, broadcasting the attendance failure.
2. The attendance system based on face recognition and voice recognition as claimed in claim 1, wherein the face image acquisition module is specifically a camera.
3. The attendance system based on face recognition and voice recognition of claim 1, wherein the face preprocessing module specifically comprises:
the graying unit is used for graying the color face image acquired by the face image acquisition module to obtain a gray face image;
the Gabor feature extraction unit is used for processing the gray face image by adopting a Gabor filter to obtain a Gabor feature image;
and the dimension reduction unit is used for reducing the dimension of the Gabor characteristic image by adopting a principal component analysis method to obtain a low-dimensional Gabor characteristic image.
4. The attendance system based on face recognition and voice recognition of claim 3, wherein the Gabor feature extraction unit is specifically configured to use a Gabor filter to perform gray-scale face image processingProcessing to obtain Gabor characteristic imageWherein, representing convolution operation for gray face image pixel,is a function of a Gabor filter, anIn the formula, mu and nu respectively represent the direction and the scale of the Gabor kernel, | | | | represents norm, k → μ , ν = k ν e jφ μ , k ν = k max f ν , φ μ = π μ N φ , σ＝2π， k m a x = π 2 , f ν = 2 , Nφ＝8。
5. the attendance system based on face recognition and voice recognition of claim 3, wherein the dimension reduction unit is specifically configured to:
according to the optimization problem: solving a group of projection matrixes V to maximize the total scattering of matrixes projected on the Gabor characteristic image, and establishing an objective function J (V) -maxTr (S)V) (ii) a Wherein S isVCovariance matrix, Tr (S), representing Gabor feature imageV) Denotes SVThe trace of (2);
calculating to obtain a projection matrix V according to the optimization problem;
and performing projection dimensionality reduction on the Gabor characteristic image according to the projection matrix V to obtain a low-dimensional Gabor characteristic image.
6. The attendance system based on face recognition and voice recognition of claim 4 wherein the Gabor kernel has a direction μ ═ 8.
7. The attendance system based on face recognition and voice recognition of claim 4 wherein the Gabor kernel has a dimension v-5.
CN201510822481.9A 2015-11-24 2015-11-24 Work attendance system based on face identification and voice recognition Pending CN105488859A (en)

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Cited By (7)

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Publication number Priority date Publication date Assignee Title
CN106611447A (en) * 2016-12-30 2017-05-03 首都师范大学 Work attendance method and apparatus
CN106855940A (en) * 2016-11-23 2017-06-16 河池学院 A kind of face identification system based on robot
CN106940904A (en) * 2017-03-14 2017-07-11 深圳汇通智能化科技有限公司 Attendance checking system based on recognition of face and speech recognition
CN108053508A (en) * 2017-12-25 2018-05-18 苏州誉阵自动化科技有限公司 A kind of wireless punch card system based on bluetooth
CN108053509A (en) * 2017-12-25 2018-05-18 苏州誉阵自动化科技有限公司 A kind of punch card system based on bluetooth
CN109544714A (en) * 2018-10-16 2019-03-29 广州师盛展览有限公司 A kind of people face identification based on biological characteristic is registered system
CN111985298A (en) * 2020-06-28 2020-11-24 百度在线网络技术（北京）有限公司 Face recognition sample collection method and device

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106855940A (en) * 2016-11-23 2017-06-16 河池学院 A kind of face identification system based on robot
CN106611447A (en) * 2016-12-30 2017-05-03 首都师范大学 Work attendance method and apparatus
CN106940904A (en) * 2017-03-14 2017-07-11 深圳汇通智能化科技有限公司 Attendance checking system based on recognition of face and speech recognition
CN106940904B (en) * 2017-03-14 2019-02-19 网思科技股份有限公司 Attendance checking system based on recognition of face and speech recognition
CN108053508A (en) * 2017-12-25 2018-05-18 苏州誉阵自动化科技有限公司 A kind of wireless punch card system based on bluetooth
CN108053509A (en) * 2017-12-25 2018-05-18 苏州誉阵自动化科技有限公司 A kind of punch card system based on bluetooth
CN109544714A (en) * 2018-10-16 2019-03-29 广州师盛展览有限公司 A kind of people face identification based on biological characteristic is registered system
CN111985298A (en) * 2020-06-28 2020-11-24 百度在线网络技术（北京）有限公司 Face recognition sample collection method and device

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