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Face Localization Using Illumination-dependent Face Model for Visual Speech Recognition
- in Proc. WASET 10 th International Conference on Signal and Image Processing
, 2010
"... Abstract—A robust still image face localization algorithm capable of operating in an unconstrained visual environment is proposed. First, construction of a robust skin classifier within a shifted HSV color space is described. Then various filtering operations are performed to better isolate face can ..."
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Abstract—A robust still image face localization algorithm capable of operating in an unconstrained visual environment is proposed. First, construction of a robust skin classifier within a shifted HSV color space is described. Then various filtering operations are performed to better isolate face candidates and mitigate the effect of substantial non-skin regions. Finally, a novel Bhattacharyya-based face detection algorithm is used to compare candidate regions of interest with a unique illumination-dependent face model probability distribution function approximation. Experimental results show a 90 % face detection success rate despite the demands of the visually noisy environment. Keywords—Audio-visual speech recognition, Bhattacharyya coefficient, face detection,
LEARNING AN INTELLIGIBILITY MAP OF INDIVIDUAL UTTERANCES
"... Predicting the intelligibility of noisy recordings is difficult and most current algorithms only aim to be correct on average across many recordings. This paper describes a listening test paradigm and as-sociated analysis technique that can predict the intelligibility of a specific recording of a wo ..."
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Predicting the intelligibility of noisy recordings is difficult and most current algorithms only aim to be correct on average across many recordings. This paper describes a listening test paradigm and as-sociated analysis technique that can predict the intelligibility of a specific recording of a word in the presence of a specific noise in-stance. The analysis learns a map of the importance of each point in the recording’s spectrogram to the overall intelligibility of the word when glimpsed through “bubbles ” in many noise instances. By treating this as a classification problem, a linear classifier can be used to predict intelligibility and can be examined to determine the importance of spectral regions. This approach was tested on record-ings of vowels and consonants. The important regions identified by the model in these tests agreed with those identified by a standard, non-predictive statistical test of independence and with the acoustic phonetics literature.
Analysis of Efficient Lip Reading Method for Various Languages
"... The traditional researches targeted at only one language, and there is no research to refer the language and recognition method. Moreover, a lot of modelbased methods use only an external lip or intraoral region, and tooth or tongue region is not reflected to the feature. This paper describes analys ..."
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The traditional researches targeted at only one language, and there is no research to refer the language and recognition method. Moreover, a lot of modelbased methods use only an external lip or intraoral region, and tooth or tongue region is not reflected to the feature. This paper describes analysis of efficient lip reading method for various languages. First, we applies active appearance model, and simultaneously extracts the external and internal lip contour. Then, the tooth and intraoral regions are detected. Various features from five regions are fed to the recognition process. We set four languages to be the recognition target, and recorded twenty words per each language. As the result, proposed trajectory feature based on three shape features, the area and aspect ratio of internal lip region, and area of intraoral region, was obtained the highest recognition rates of 93.6%, compared with the traditional methods and other regions. 1.
We Can Hear You with Wi-Fi!
"... Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and an ..."
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Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducingMouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear ” people talks within the radio range. Further, WiHear can simultaneously “hear”multiple people’s talks leveraging MIMO technology. We implement WiHear on both USRP N210 platform and commercial Wi-Fi infrastructure. Re-sults show that within our pre-defined vocabulary, WiHear can achieve detection accuracy of 91 % on average for single individual speaking no more than 6 words and up to 74% for no more than 3 people talking simultaneously. Moreover, the detection accuracy can be further improved by deploying multiple receivers from different angles.
We Can Hear You with Wi-Fi!
"... Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and an ..."
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Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducingMouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear ” people talks within the radio range. Further, WiHear can simultaneously “hear”multiple people’s talks leveraging MIMO technology. We implement WiHear on both USRP N210 platform and commercial Wi-Fi infrastructure. Re-sults show that within our pre-defined vocabulary, WiHear can achieve detection accuracy of 91 % on average for single individual speaking no more than 6 words and up to 74% for no more than 3 people talking simultaneously. Moreover, the detection accuracy can be further improved by deploying multiple receivers from different angles.
Date:......................
, 2013
"... The attached document may provide the author's accepted version of a published work. See Citation for details of the published work. ..."
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The attached document may provide the author's accepted version of a published work. See Citation for details of the published work.
