Learning-based detection of acne-like regions using time-lapse features

Objective evaluation of acne treatment requires observing test subjects for multiple months. To capture the appearance of acne lesions during the treatment period, a subject is photographed at imaging sessions separated by time intervals of days or weeks. The efficacy of the treatment method is evaluated by counting the number of acne lesions in the acquired skin images. Traditionally, the counting of acne lesions has been done manually. However, manual counting is unreliable and time consuming; therefore in recent years there has been an increasing interest in automatically detecting and counting acne lesions using computer-based methods. In this paper we model acne-like and non-acne regions using spatio-temporal features, and use a supervised learning approach to find the separating hyperplane between the regions in the feature space. The temporal component is an important feature because acne lesions change over time, while scars and other marks remain constant. Precise alignment is a challenge in computing meaningful temporal features. The images must be aligned to a subpixel level, exceeding the requirements of typical face alignment algorithms. We have acquired and aligned a time series acne dataset by imaging a human subject with facial acne under the same illumination and pose on 39 different days over a period of three months. The resulting time-lapse video of skin with precision alignment is the first of its kind and impressively demonstrates the temporal evolution of acne lesions. We use this registered time-lapse set to train and test an acne lesion classifier.