Face detection has been well studied in terms of accuracy and speed. However, required memory size reduction is still poorly studied, which is becoming a critical issue as platforms for face detection go tiny. In this paper, we propose a novel compact weak classifier using Adaptive Look-Up-Table (ALUT) for face detection on resource-constrained devices such as wearable sensor nodes. ALUT gives good approximation of log-likelihood [3] with fewer data, thus enabling the drastic reduction of classifier data size, keeping high accuracy and low computation cost. To generate an optimal ALUT, a new cost function called Weighted Sum of Absolute Difference (WSAD) is also proposed for further improvement. In our experiment, the classifier data size is reduced by 43% and the computation cost is reduced by 15% with same accuracy, compared to a conventional fixed LUT classifier.
['Yuya Hanai', 'Tadahiro Kuroda']
Face detection through compact classifier using Adaptive Look-Up-Table