Incremental face recognition for large-scale social network services

Choi, Kwontaeg; Toh, Kar-Ann; Byun, Hyeran

doi:10.1016/j.patcog.2012.02.002

Cited by 45 publications

(24 citation statements)

References 46 publications

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“…For example, artificial neural networks (i.e., systems that learn from data) have been used in different biometric applications involving pattern classification and identification (of a human (Dinkar andSambyal 2012, Melin et al 2012), of driver (Wu and Ye 2009), of finger-vein patterns (Wu and Liu 2011), of iris recognition (Sibai et al 2011), of human action (Youssef and Asari 2013), of gait (Zeng and Wang 2012), of the face (Connolly et al 2013;Kuo et al 2011;Choi et al 2012;Banerjee and Datta 2013;Lin and Lin 2013;Müller et al 2013), of the hand (Michael et al 2008), of the skin (Zaidan et al 2014), by keystroke (Uzun and Bicakci 2012) and by gesture, speech, handwritten text recognition and the like). Various biometric systems are being developed in such a manner (face recognition, fingerprint identification, hand geometry biometrics, retina scan, iris scan, signature, voice analysis, palm vein authentication and others).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…The major problem in such applications is to deal efficiently with the growing number of samples as well as local appearance variations caused by diverse environments for the millions of users over time (Choi et al 2012). Choi et al (2012) focus on developing an incremental face recognition method for Twitter application. Particularly, a dataindependent feature extraction method is proposed via binarization of a Gabor filter.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…Finally, an incremental neural network is applied to learn the reduced Gabor features. Choi et al (2012) apply the proposed method to a novel application which notifies new photograph uploading to related users without having their ID being identified.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…The performance of SVM and ELM was reported to be similar [19]. Incremental learning of ELM was previously studied in [20,21]. They applied it for human action recognition and face recognition.…”

Section: Introductionmentioning

confidence: 89%

Improving session-to-session transfer performance of motor imagery-based BCI using adaptive extreme learning machine

Bamdadian

Guan

Ang

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Non-stationarity of electroencephalograph (EEG) data from session-to-session transfer is one of the challenges for EEG-based brain-computer interface systems, which can inversely affect their performance. Among methods proposed to address non-stationarity, adaptation is a promising method. In this study, an adaptive extreme learning machine (AELM) is proposed to update the initial classifier from the calibration session by using chunks of EEG data from the evaluation session whereby the common spatial pattern (CSP) algorithm is used to extract the most discriminative features. The effectiveness of the proposed algorithm is on motor imagery data collected from 12 healthy subjects during a calibration session and an evaluation session on a separate day. The results from the proposed AELM were compared with non-adaptive ELM and SVM classifiers. The results showed that AELM was significantly better (p=0.03). Moreover, the results also showed that accumulating the evaluation session data and using them for adapting the classifier will significantly improve the performance (p=0.001). Hence, the proposed AELM is effective in addressing the non-stationarity of EEG signal for online BCI systems.

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“…Wang 3 -network) to meet the challenge of the so-called big data [15]. In addition, ELM has been put into diverse applications such as speaker recognition [16], neuroimage data classification [17], security assessment [18], data privacy [19], EEG and seizure detection [20], image quality assessment [21], image super-resolution [22], FPGA [23], face recognition [24], and human action recognition [25].…”

Section: Introductionmentioning

confidence: 99%