The Viola–Jones face detection algorithm was (and still is) a quite popular face detector. In spite of the numerous face detection techniques that have been recently presented, there are many research works that are still based on the Viola–Jones algorithm because of its simplicity. We study the influence of a set of blind preprocessing methods on the face detection rate using the Viola–Jones algorithm. We focus on two aspects of improvement, specifically badly illuminated faces and blurred faces. Many methods for lighting invariant and deblurring are used in order to improve the detection accuracy. We want to avoid using blind preprocessing methods that may obstruct the face detector. To that end, we perform two sets of experiments. The first set is performed to avoid any blind preprocessing method that may hurt the face detector. The second set is performed to study the effect of the selected preprocessing methods on images that suffer from hard conditions. We present two manners of applying the preprocessing method to the image prior to being used by the Viola–Jones face detector. Five different datasets are used to draw a coherent conclusion about the potential improvement caused by using prior enhanced images. The results demonstrate that some of the preprocessing methods may hurt the accuracy of the Viola–Jones face detection algorithm. However, other preprocessing methods have an evident positive impact on the accuracy of the face detector. Overall, we recommend three simple and fast blind photometric normalization methods as a preprocessing step in order to improve the accuracy of the pretrained Viola–Jones face detector.
Plant aliveness is proven through laboratory experiments and special scientific instruments. We aim to detect the degree of animation of plants based on the magnification of the small color changes in the plant’s green leaves using the Eulerian video magnification. Capturing the video under a controlled environment, e.g., using a tripod and direct current light sources, reduces camera movements and minimizes light fluctuations; we aim to reduce the external factors as much as possible. The acquired video is then stabilized and a proposed algorithm is used to reduce the illumination variations. Finally, the Euler magnification is utilized to magnify the color changes on the light invariant video. The proposed system does not require any special purpose instruments as it uses a digital camera with a regular frame rate. The results of magnified color changes on both natural and plastic leaves show that the live green leaves have color changes in contrast to the plastic leaves. Hence, we can argue that the color changes of the leaves are due to biological operations, such as photosynthesis. To date, this is possibly the first work that focuses on interpreting visually, some biological operations of plants without any special purpose instruments.