• Title/Summary/Keyword: Video Face Recognition

Search Result 110, Processing Time 0.038 seconds

Recognition of dog's front face using deep learning and machine learning (딥러닝 및 기계학습 활용 반려견 얼굴 정면판별 방법)

  • Kim, Jong-Bok;Jang, Dong-Hwa;Yang, Kayoung;Kwon, Kyeong-Seok;Kim, Jung-Kon;Lee, Joon-Whoan
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.21 no.12
    • /
    • pp.1-9
    • /
    • 2020
  • As pet dogs rapidly increase in number, abandoned and lost dogs are also increasing in number. In Korea, animal registration has been in force since 2014, but the registration rate is not high owing to safety and effectiveness issues. Biometrics is attracting attention as an alternative. In order to increase the recognition rate from biometrics, it is necessary to collect biometric images in the same form as much as possible-from the face. This paper proposes a method to determine whether a dog is facing front or not in a real-time video. The proposed method detects the dog's eyes and nose using deep learning, and extracts five types of directional face information through the relative size and position of the detected face. Then, a machine learning classifier determines whether the dog is facing front or not. We used 2,000 dog images for learning, verification, and testing. YOLOv3 and YOLOv4 were used to detect the eyes and nose, and Multi-layer Perceptron (MLP), Random Forest (RF), and the Support Vector Machine (SVM) were used as classifiers. When YOLOv4 and the RF classifier were used with all five types of the proposed face orientation information, the face recognition rate was best, at 95.25%, and we found that real-time processing is possible.

A study of Real-Time Face Recognition using Web CAM and Ideal Hair style Adaption Method (웹캠을 이용한 실시간 얼굴인식과 이상적 헤어스타일 적용방법에 관한 연구)

  • Kang, Nam-Soon
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.11 no.2
    • /
    • pp.532-539
    • /
    • 2010
  • This paper suggests the system for searching and application is to be in combination between existing hair art area and Image/Video processing area. This proposed system usually saves various hair types into a database, then, users send images of their face over the internet by using WebCam. Finally, they can find the hair types for users.

Iris Detection at a Distance by Non-volunteer Method (비강압적 방법에 의한 원거리에서의 홍채 탐지 기법)

  • Park, Kwon-Do;Kim, Dong-Su;Kim, Jeong-Min;Song, Young-Ju;Koh, Seok-Joo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2018.05a
    • /
    • pp.705-708
    • /
    • 2018
  • Among biometrics commercialized for security, iris recognition technology has the most excellent security for the probability of the match between individuals is the lowest. Current commercialized iris recognition technology has excellent recognition ability, but this technology has a fatal drawback. Without the user's active cooperation, it cannot recognize the iris correctly. To make up for this weakness, recent trend of iris recognition development mounts a non-volunteering, unconstrained method. According to this information, the objective of this research is developing a module that can identify people iris from a video acquired by high performance infrared camera in a range of 3m and in a involuntary way. For this, we import images from the video and find people's face and eye positions from the images using Haar classifier trained through Cascade training method. finally, we crop the iris by Hough circle transform and compare it with data from the database to identify people.

  • PDF

Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators

  • Kim, Hye-Jin;Kim, Dohyung;Lee, Jaeyeon;Jeong, Il-Kwon
    • ETRI Journal
    • /
    • v.37 no.2
    • /
    • pp.395-405
    • /
    • 2015
  • We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.

Major Character Extraction using Character-Net (Character-Net을 이용한 주요배역 추출)

  • Park, Seung-Bo;Kim, Yoo-Won;Jo, Geun-Sik
    • Journal of Internet Computing and Services
    • /
    • v.11 no.1
    • /
    • pp.85-102
    • /
    • 2010
  • In this paper, we propose a novel method of analyzing video and representing the relationship among characters based on their contexts in the video sequences, namely Character-Net. As a huge amount of video contents is generated even in a single day, the searching and summarizing technologies of the contents have also been issued. Thereby, a number of researches have been proposed related to extracting semantic information of video or scenes. Generally stories of video, such as TV serial or commercial movies, are made progress with characters. Accordingly, the relationship between the characters and their contexts should be identified to summarize video. To deal with these issues, we propose Character-Net supporting the extraction of major characters in video. We first identify characters appeared in a group of video shots and subsequently extract the speaker and listeners in the shots. Finally, the characters are represented by a form of a network with graphs presenting the relationship among them. We present empirical experiments to demonstrate Character-Net and evaluate performance of extracting major characters.

Enterprise Human Resource Management using Hybrid Recognition Technique (하이브리드 인식 기술을 이용한 전사적 인적자원관리)

  • Han, Jung-Soo;Lee, Jeong-Heon;Kim, Gui-Jung
    • Journal of Digital Convergence
    • /
    • v.10 no.10
    • /
    • pp.333-338
    • /
    • 2012
  • Human resource management is bringing the various changes with the IT technology. In particular, if HRM is non-scientific method such as group management, physical plant, working hours constraints, personal contacts, etc, the current enterprise human resources management(e-HRM) appeared in the individual dimension management, virtual workspace (for example: smart work center, home work, etc.), working time flexibility and elasticity, computer-based statistical data and the scientific method of analysis and management has been a big difference in the sense. Therefore, depending on changes in the environment, companies have introduced a variety of techniques as RFID card, fingerprint time & attendance systems in order to build more efficient and strategic human resource management system. In this paper, time and attendance, access control management system was developed using multi camera for 2D and 3D face recognition technology-based for efficient enterprise human resource management. We had an issue with existing 2D-style face-recognition technology for lighting and the attitude, and got more than 90% recognition rate against the poor readability. In addition, 3D face recognition has computational complexities, so we could improve hybrid video recognition and the speed using 3D and 2D in parallel.

Design and Implementation of a Real-Time Lipreading System Using PCA & HMM (PCA와 HMM을 이용한 실시간 립리딩 시스템의 설계 및 구현)

  • Lee chi-geun;Lee eun-suk;Jung sung-tae;Lee sang-seol
    • Journal of Korea Multimedia Society
    • /
    • v.7 no.11
    • /
    • pp.1597-1609
    • /
    • 2004
  • A lot of lipreading system has been proposed to compensate the rate of speech recognition dropped in a noisy environment. Previous lipreading systems work on some specific conditions such as artificial lighting and predefined background color. In this paper, we propose a real-time lipreading system which allows the motion of a speaker and relaxes the restriction on the condition for color and lighting. The proposed system extracts face and lip region from input video sequence captured with a common PC camera and essential visual information in real-time. It recognizes utterance words by using the visual information in real-time. It uses the hue histogram model to extract face and lip region. It uses mean shift algorithm to track the face of a moving speaker. It uses PCA(Principal Component Analysis) to extract the visual information for learning and testing. Also, it uses HMM(Hidden Markov Model) as a recognition algorithm. The experimental results show that our system could get the recognition rate of 90% in case of speaker dependent lipreading and increase the rate of speech recognition up to 40~85% according to the noise level when it is combined with audio speech recognition.

  • PDF

Implementation of Character and Object Metadata Generation System for Media Archive Construction (미디어 아카이브 구축을 위한 등장인물, 사물 메타데이터 생성 시스템 구현)

  • Cho, Sungman;Lee, Seungju;Lee, Jaehyeon;Park, Gooman
    • Journal of Broadcast Engineering
    • /
    • v.24 no.6
    • /
    • pp.1076-1084
    • /
    • 2019
  • In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

Efficient Storage and Retrieval for Automatic Indexing of Persons in Videos (동영상 등장인물의 자동색인을 위한 효율적인 저장과 검색 방법)

  • Kim, Jin-Seung;Han, Yong-Koo;Lee, Young-Koo
    • Journal of Korea Multimedia Society
    • /
    • v.14 no.8
    • /
    • pp.1050-1060
    • /
    • 2011
  • With increasing need for indexing of persons in a large video database, automatic indexing has been attracting great interest which takes advantage of automatic tagging instead of the time-consuming and costly manual tagging. However, automatic indexing approach should provide a degree of recognition proximity because it cannot identify the persons with accuracy of 100%. In this paper, we propose an efficient storage method for storing posting lists efficiently and a novel ranking technique of ordering relevant videos for efficient retrieval. Through experiment evaluations we have shown that our storage method exhibits good performance in compressing the posting list. We have also shown that the proposed ranking method is effective for finding relevant videos.

Robust Facial Expression-Recognition Against Various Expression Intensity (표정 강도에 강건한 얼굴 표정 인식)

  • Kim, Jin-Ok
    • The KIPS Transactions:PartB
    • /
    • v.16B no.5
    • /
    • pp.395-402
    • /
    • 2009
  • This paper proposes an approach of a novel facial expression recognition to deal with different intensities to improve a performance of a facial expression recognition. Various expressions and intensities of each person make an affect to decrease the performance of the facial expression recognition. The effect of different intensities of facial expression has been seldom focused on. In this paper, a face expression template and an expression-intensity distribution model are introduced to recognize different facial expression intensities. These techniques, facial expression template and expression-intensity distribution model contribute to improve the performance of facial expression recognition by describing how the shift between multiple interest points in the vicinity of facial parts and facial parts varies for different facial expressions and its intensities. The proposed method has the distinct advantage that facial expression recognition with different intensities can be very easily performed with a simple calibration on video sequences as well as still images. Experimental results show a robustness that the method can recognize facial expression with weak intensities.