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For human beings, sleep is a key requirement. The secret of humankind's physical well-being is sleep. In a study on sleep, researchers have proved that adults from the age of eighteen and above must get seven to nine hours of sleep a day. Drowsiness is the root cause of the hazardous road accidents. If drivers are notified as drowsy at the correct instant of time, we can prevent the majority of road accidents that took place in the world. New strategies are introduced by the researchers to detect the drowsiness of the driver and each technology has its own merit and demerit. This paper uses Python and Dlib models to build a drowsiness identification model. We aim to integrate both face detection and head pose detection which makes this an ideal detection method. In the proposed system, a laptop is used, using which real-time video is recorded. Head-pose detection along with face detection helps to increase accuracy. For dataset video input, the proposed system gives a maximum accuracy rate of 94.51%.

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Media storage devices experience browsing difficulty for users. One cause is the rapid growth in Digital Photography that results in a large database of stored Digital Photographs. Managing these photographs manually becomes a complex task. Hence there is a need to organize the Digital Photographs automatically. The application proposed in this paper solves this by using algorithms for image understanding and techniques for interpreting the metadata present in the Digital Photographs. Using the algorithms and the metadata, the application organizes the images based on People, Event, Location and semantic meaning. Hence the application provides an intuitive way for organizing and browsing Digital Photographs automatically.