• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.1-7
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• 2010

With the widespread use of vision-based surveillance systems, the capability for person identification is now an essential component. However, the CCTV cameras used in surveillance systems tend to produce relatively low-resolution images, making it difficult to use face recognition techniques for person identification. Therefore, an algorithm is proposed for person identification in CCTV camera images based on the clothing. Whenever a person is authenticated at the main entrance of a building, the clothing feature of that person is extracted and added to the database. Using a given image, the clothing area is detected using background subtraction and skin color detection techniques. The clothing feature vector is then composed of textural and color features of the clothing region, where the textural feature is extracted based on a local edge histogram, while the color feature is extracted using octree-based quantization of a color map. When given a query image, the person can then be identified by finding the most similar clothing feature from the database, where the Euclidean distance is used as the similarity measure. Experimental results show an 80% success rate for person identification with the proposed algorithm, and only a 43% success rate when using face recognition.

• Journal of Korean Academy of Nursing
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• v.44 no.2
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• pp.198-208
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• 2014

Purpose: This research was an empirical study designed to identify precursors and interaction effects related to nurses' patient identification behavior. A multilevel analysis methodology was used. Methods: A self-report survey was administered to registered nurses (RNs) of a university hospital in South Korea. Of the questionnaires, 1114 were analyzed. Results: The individual-level factors that had a significantly positive association with patient identification behavior were person-organization value congruence, organizational commitment, occupational commitment, tenure at the hospital, and tenure at the unit. Significantly negative group-level precursors of patient identification behavior were burnout climate and the number of RNs. Two interaction effects of the person-organization value congruence climate were identified. The first was a group-level moderating effect in which the negative relationship between the number of RNs and patient identification behavior was weaker when the nursing unit's value congruence climate was high. The second was a cross-level moderating effect in which the positive relationship between tenure at the unit and patient identification behavior was weaker when value congruence climate was high. Conclusion: This study simultaneously tested both individual-level and group-level factors that potentially influence patient identification behavior and identified the moderating role of person-organization value congruence climate. Implications of these results are discussed.

• Korean Journal of Oriental Medicine
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• v.13 no.1 s.19
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• pp.109-114
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• 2007

The high position condition 10 escape which the obesity person appeals. Obesity Pattern-Identification question it will yell and 243 subjects which to the obesity in the patient of 517 subjects which draw up correspond. (longitude obesity 153 person, altitude obesity 90 person) against it analyzes. In order to analyze the reliability of the items which diagnose each Pattern-Identification it used Cronbach alpha coefficient and escape it did the alpha of each item. Alpha value of each Pattern-Identification than appears more highly the item which it will be able to consider an elimination in the item which decreases a reliability. In that phlegm-retention syndrome is bigger alpha coefficient 0.784 than 'meal quantity is few'(0.787) a possibility of seeing in the item which decreases a reliability to the place where it diagnoses it puts in phlegm-retention syndrome.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.53-64
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• 2017

Korean Identification card or driver license is usually used to verify one's identity in Korea. These are also used as an adult certification. Since the form of these ID card is an analog and it needs to be checked with naked eyes, it might be used maliciously. Someone who's got someone else's ID card can do other things. Therefore, it must be reported rapidly when ID card is lost or stolen. The most serious problem might be occurred when they do not recognize and report the loss. They might suffer from pecuniary or mental damage such as opening a mobile phone service, providing loan or credit card, opening a personal checking account, etc. Thus, this study suggests and compares the ways of avoiding these problems. First, the most effective way is to send the authorization code via mobile phones in consideration of build-up period and cost. The person in charge of business processing department using ID card sends the authorization code via registered mobile phone. The owners submits it to the person and their identifications are confirmed. Next effective way is that the person in charge of business processing department using ID card sends text messages via registered mobile phone. Lastly, the most ineffective way is to introduce and implement the electronic ID card ultimately even though it is expensive and takes a long time to build up the system.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.251-254
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• 2000

This paper proposes an enhanced algorithm for person identification system utilizing hand vein pattern. The conventional algorithm does not cope with distortion caused by image rotation caused by misplaced hands on the imaging device. A straightforward approach to consider the rotaional compensation required too much computational load, thus, we devised an approach to expect the rotation direction along with image translation, reducing the compuational requirement dramatically In this paper, we present the details of the algorithm with experimental results with the new algorithm.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.247-250
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• 2000

This paper proposes a finger crease pattern identification algorithm utilizing a clustering method. The algorithms has been developed for the use of biometric person identification system. Since the finger crease pattern may be well-imaged utilizing low cost imaging devices such as low-end CCD camera with LED lighting, the feasibility of commercialization of the algorithm and the system utilizing the algorithm may be well justified if the finger crease pattern is a reasonable choice for the biometric feature. In this paper, we exploit this possibility and show the potential of using the finger crease pattern as a feature for biometric person identification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.15-23
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• 2017

The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.