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Over the past decades, facial expression recognition (FER) has become an interesting research area and achieved substantial progress in computer vision. FER is to detect human emotional state related to biometric traits. Developing a machine based human FER system is a quite challenging task. Various FER systems are developed by analysing facial muscle motion and skin deformation based algorithms. In conventional FER system, the developed algorithms work on the constrained database. In the unconstrained environment, the efficacy of existing algorithms is limited due to certain issues during image acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analysing the efficacy of the recognition techniques. Moreover, this survey will assist researchers in understanding the strategies and innovative methods that address the issues in a real-time application. Finally, the review presents the challenges encountered by FER system along with the future direction.

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Automated analysis of human chromosomes is a necessary procedure to attain karyotyping and it is highly effective in cytology analysis to detect birth defects in metaspread chromosomes. In this, chromosomes are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate chromosome segmentation. Despite many years of research in this field, accurate segmentation and classification remains a challenge in the presence of cell clusters and pathologies. Many classification methods focused on hand crafted features, such as length, centromere positions. In this manuscript, proposed method focused on chromosome classification based on deep features using convolutional neural network. It is subsequently trained on various chromosome datasets consisting of adaptively resampled image patches. In the testing phase, average the prediction scores of a similar set of image patches is performed. The proposed method is evaluated on different overlapped, nonoverlapped chromosomes and normal, abnormal datasets. Proposed method better performs than previous algorithms in classification accuracy with 98.7%, area under the curve AUC is 0.97 values, and abnormality detection accuracy is 98.4%.

Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.

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