Tools and Libraries: Python, OpenCV, NumPy, Scikit-image, Matplotlib, SciPy, Haarcascades, Spyder IDE, Anaconda.
Summary: Darkness is the inverse state of the brightness, is obtained as an absence of noticeable light and illumination. Generally, face detection applications cannot detect any human face in a dark image, where the image has captured from the dark environment or dark night. In this manuscript, we demonstrate our experiment, where we use Contrast Stretching, Histogram Equalization and Adaptive Equalization techniques for detecting any human face in any dark image. In this paper, we also illustrate our proposed algorithm, working procedure and differentiate the pixel intensity of different stage of image processing. We essentially do this research from an application perspective, where a software application detects the human face from a dark photo or a very low-contrast image and the photo has been captured from an excessive dark environment.
