Face Classification Using Cascade Facial Detection and Convolutional Neural Network

Yu, Jong Min; Sim, Kwee-Bo

doi:10.5391/jkiis.2016.26.1.070

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…However, this LeNet-5 was modified to increase the classification rates. Moreover, this LeNet-5 has more layers than conventional LeNet-5 [1,2,11].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…In 2016, Yu and Kim [1] used the Viola-Jones algorithm to extract and classify subject faces. The extracted faces of subjects were classified using the classifying algorithm.…”

Section: Viola-jones Object Detection Algorithmmentioning

confidence: 99%

“…Thus, researchers on robots or AI are developing diverse robots to recognize human expression, emotions, and speech. Such research is called human-robot interaction (HRI) [1,2]. HRI can be applied to various fields such as factories, hospitals, and amusement parks.…”

Section: Introductionmentioning

confidence: 99%

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Visual Speech Recognition of Korean Words Using Convolutional Neural Network

Lee

Park

et al. 2019

IJFIS

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In recent studies, speech recognition performance is greatly improved by using HMM and CNN. HMM is studying statistical modeling of voice to construct an acoustic model and to reduce the error rate by predicting voice through image of mouth region using CNN. In this paper, we propose visual speech recognition (VSR) using lip images. To implement VSR, we repeatedly recorded three subjects speaking 53 words chosen from an emergency medical service vocabulary book. To extract images of consonants, vowels, and final consonants in the recorded video, audio signals were used. The Viola-Jones algorithm was used for lip tracking on the extracted images. The lip tracking images were grouped and then classified using CNNs. To classify the components of a syllable including consonants, vowels, and final consonants, the structure of the CNN used VGG-s and modified LeNet-5, which has more layers. All syllable components were classified, and then the word was found by the Euclidean distance. From this experiment, a classification rate of 72.327% using 318 total testing words was obtained when VGG-s was used. When LeNet-5 applied this classifier for words, however, the classification rate was 22.327%.

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“…However, this LeNet-5 was modified to increase the classification rates. Moreover, this LeNet-5 has more layers than conventional LeNet-5 [1,2,11].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…In 2016, Yu and Kim [1] used the Viola-Jones algorithm to extract and classify subject faces. The extracted faces of subjects were classified using the classifying algorithm.…”

Section: Viola-jones Object Detection Algorithmmentioning

confidence: 99%

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Visual Speech Recognition of Korean Words Using Convolutional Neural Network

Lee

Park

et al. 2019

IJFIS

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Grading meat quality of Hanwoo based on SFTA and AdaBoost

Cho¹,

Kim²,

Jang³

et al. 2016

Journal of Korean Institute of Intelligent Systems

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This paper proposes a grade prediction method to measure meat quality in Hanwoo (Korean Native Cattle) using classification and feature extraction algorithms. The applied classification algorithm is an AdaBoost and the texture features of the given ultrasound images are extracted using SFTA. In this paper, as an initial phase, we selected ultrasound images of Hanwoo for verifying experimental results; however, we ultimately aimed to develop a diagnostic decision support system for human body scan using ultrasound images. The advantages of using ultrasound images of Hanwoo are: accurate grade prediction without butchery, optimizing shipping and feeding schedule and economic benefits. Researches on grade prediction using biometric data such as ultrasound images have been studied in countries like USA, Japan, and Korea. Studies have been based on accurate prediction method of different images obtained from different machines. However, the prediction accuracy is low. Therefore, we proposed a prediction method of meat quality. From the experimental results compared with that of the real grades, the experimental results demonstrated that the proposed method is superior to the other methods.

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