Person identification system for future digital tv with intelligence

Hwang, Min-Cheol; Ha, Le Thanh; Kim, Nam-Hyeong; Park, Chun-Su; Ko, Sung-Jea

doi:10.1109/tce.2007.339528

Cited by 17 publications

(2 citation statements)

References 19 publications

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“…Furthermore, video cameras have recently become an integral part of many consumer devices [3] and can be used for capturing facial images for recognition of people and their emotions. This ability to recognize emotions can enable customized applications [4], [5]. Even though much work has already been done on automatic facial expression recognition [6], [7], higher accuracy with reasonable speed still remains a great challenge [8].…”

Section: Introductionmentioning

confidence: 99%

Robust Facial Expression Recognition Based on Local Directional Pattern

Jabid¹

2010

ETRI J

314

205

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Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance‐based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well‐known machine learning methods, template matching and support vector machine, are used for classification using the Cohn‐Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance‐based feature descriptors.

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Section: Introductionmentioning

confidence: 99%

Robust Facial Expression Recognition Based on Local Directional Pattern

Jabid¹

2010

ETRI J

314

205

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“…Deep learning [16] is a branch of machine learning. The biggest difference is that machine learning uses ready-made human knowledge to extract features from a large amount of training data then uses these features to train the model so that the model can judge.…”

Section: Deep Learningmentioning

confidence: 99%

Drone for Dynamic Monitoring and Tracking with Intelligent Image Analysis

Yao¹,

Kao²,

Lin³

2023

Intelligent Automation &Amp; Soft Computing

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Traditional monitoring systems that are used in shopping malls or community management, mostly use a remote control to monitor and track specific objects; therefore, it is often impossible to effectively monitor the entire environment. When finding a suspicious person, the tracked object cannot be locked in time for tracking. This research replaces the traditional fixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person. This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system. In this article, we proposed a TIMT (The Intelligent Monitoring and Tracking) algorithm which can make the drone have smart surveillance and tracking capabilities. It combined with Artificial Intelligent (AI) face recognition technology and the OpenPose which is able to monitor the physical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote control interface of the drone. This system is highly agile and could be adjusted immediately to any angle and screen that we monitor. Therefore, the system could find abnormal conditions immediately and track and monitor them automatically. That is the system can immediately detect when someone invades the home or community, and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved. Experimental results show that the intelligent monitoring and tracking drone system has an excellent performance, which not only dramatically reduces the number of monitors and the required equipment but also achieves perfect monitoring and tracking.

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