Face and Its Features Detection during Nap

A quality sleep at night plays a vibrant role in healthy life. 7-8 hours quality sleep at the right times especially at night help human to maintain a proper physical and mental health. While sleeping, it has been incorporated that facial muscles contraction/extraction especially in eyes regions are the most common absorbed features while sleeping. This paper presents a preprocessing outcome of detecting a person face and facial features while taking nap. Face Detection algorithms known as Ada-boost and Local Binary Pattern (LBP) has been used to detect the facial regions and its features. As these algorithm work for frontal faces, so when person is taking nap in soldier position and a face orientation is in $120^{\circ}-60^{\circ}$, Ada-boost and LBP is able to detect face and its features. Results shows that LBP face/features detection accuracy is higher than Ada-boost. This pre-processing study/results help us in designing the novel post processing algorithms to classify sleep stages for overnight sleep monitoring using image processing that will be unobtrusive as compared to existing techniques.