Abstract
With the growing importance of age estimation in the recent years, Researchers have been trying to use different human body biometrics to estimate the age of a person. Face, gait and speech are the three main biometric traits which have been reported to investigate the human age successfully. Each feature has specific characteristics which employ the prediction of age. Like Wrinkles, skin and shape of the face; speed, head to body ratio and height of the gait; and pitch and heaviness of the speech define the baselines for the age estimation. We have compared these three features and evaluated their performance. Conventional techniques have been used from the literature and experimental results are compared in terms of MAE and accuracy. Face is found to have most detailed features to predict the age and hence minimum mean absolute error of 5.36. It is followed by gait and then speech which are found to have mean absolute error of 6.57 and 6.62 respectively.
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Punyani, P., Gupta, R., Kumar, A. (2018). A Comparison Study of Face, Gait and Speech Features for Age Estimation. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_34
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DOI: https://doi.org/10.1007/978-981-10-4765-7_34
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