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This paper summarizes work done on real-time human age-group estimation based on frontal facial images. Our approach relies on detecting visible ageing effects, such as facial skin texture. This information is described using uniform Local Binary Patterns (LBP) and the estimation is done using the K-Nearest Neighbour classifier. In the current work, the system is trained using the FERET dataset. The training data is divided into five main age groups. Facial images captured in real-time using the Microsoft Kinect RGB data are used to classify the subjects age into one of the five different age groups. An accuracy of 81% was achieved on the live testing data. In the proposed approach, only facial regions affected by the ageing process are used in the face description. Moreover, the use of uniform Local Binary Patterns is evaluated in the context of facial description and age-group estimation. Results show that the uniform LBP depicts most of the facial texture information. That led to speeding up the entire process as the feature vector’s length has been reduced significantly, which optimises the process for real-time applications.

Real-time Human Age Estimation based on Facial Images using Uniform Local Binary Patterns