Abstract-Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results. To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs. The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies. Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies. Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world. IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related applications.
Pitman Shorthand Language (PSL) is a phonetic based language developed in 1837 to translate speech into text. Recognition of text recorded in PSL is an interesting research problem. The PSL has the practical advantage of high speed of recording, more than 120-200 words per minute, because of which it is universally acknowledged. This recording medium has its continued existence inspite of considerable developments in speech processing systems, which are not universally established yet. In order to exploit the vast transcribing potential of PSL a new area of research on automation of PSL processing is conceived.In this work, we have proposed the secant based method for recognition of PSL characters. The work comprises of preprocessing such as thinning and filling, determination of end points of the handwritten strokes. Slope of the strokes are determined using end points of the stroke. Characters are classified based on the estimated slopes of secants and other features such as stroke type and thickness. The vowels are classified based on the vowel type such as dash or dot and thickness and position with respect to a stroke. The proposed work is thoroughly tested for a large number of handwritten strokes. The recognition rates are estimated and found to be in the range of 60 to 95 %.
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