Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

Degraded historical document images face many challenges in the process of optical character recognizing or word spotting, even after applying the traditional binarization techniques. In this paper, we propose a K-means based clustering technique for adaptive binarization of degraded document images. For validation of test results, we have used the recent dataset of Handwritten counterpart of Document Image Binarization Contest (H-DIBCO'16) comprising of highly degraded handwritten document images and computed detailed results of each image. In order to corroborate verification and validation, the experimental results are compared with three top winning ones in the contest and other prominent techniques in the literature. Experimental results reveal outstanding performance in the four evaluation measures compared with the top winners of the competition, claiming its effectiveness and validity conformance.