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Detection of persons and evaluation of gender and age in image data
Dobiš, Lukáš ; Vičar, Tomáš (oponent) ; Kolář, Radim (vedoucí práce)
This master thesis describes an approach for automated human recognition by using convolutional neural networks (CNN) to perform facial analysis of persons face in image data. The predicted biometric indicators are following: age, gender, facial landmarks and facial expression. CNN architectures with pretrained weights for each task are described. Age estimation CNN has new weights trained and freezed, then has added new LSTM layers into its architecture. New LSTM layers are trained and tested on newly created video data set. Test results indicate improved age prediction accuracy. Solution for human recognition inference with single image and time series variants, in form of script with interconnected CNNs is explained, and its inference speed performance supports further proposed expansion plans for live video inference.
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Automated Human Recognition From Image Data
Dobiš, Lukáš
This paper describes an approach for automated human recognition by using convolutional neural networks (CNN) to perform facial analysis of persons face from image data. The predicted biometric indicators are following: age, gender, facial landmarks and facial expression. Network architectures with pretrained weights for each task are described. Script of interconnected CNN is explained and its results support further proposed expansion plans for live video inference.
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Detection of persons and evaluation of gender and age in image data
Dobiš, Lukáš ; Vičar, Tomáš (oponent) ; Kolář, Radim (vedoucí práce)
This master thesis describes an approach for automated human recognition by using convolutional neural networks (CNN) to perform facial analysis of persons face in image data. The predicted biometric indicators are following: age, gender, facial landmarks and facial expression. CNN architectures with pretrained weights for each task are described. Age estimation CNN has new weights trained and freezed, then has added new LSTM layers into its architecture. New LSTM layers are trained and tested on newly created video data set. Test results indicate improved age prediction accuracy. Solution for human recognition inference with single image and time series variants, in form of script with interconnected CNNs is explained, and its inference speed performance supports further proposed expansion plans for live video inference.
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