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The authors propose a novel-makeup detection approach that assists face recognition systems to achieve a higheraccuracy rate while dealing with makeup images. Makeup features are defined in this work using biologically inspired features (BIFs). To establish makeup depictive features more specifically, colour and texture features are essential to be extracted from images. Hence, they create makeup depictive features in the last complex layer of BIFs (C2) as average skin tone (AST) and a histogram of oriented gradient (HOG), where AST is representative of colour and HOG exhibits texture. The proposed makeup BIFs are extracted from grayscale images and instead of breaking the face image into several patches, the whole face image is employed. This resulted in the performance acceleration as well as higher accuracy rate compared with state-of-the-art makeup detection schemes. Subsequently, they employ a machine learning scheme to train the makeup detection system by feeding it makeup and non-makeup labelled images. They exploit the correlation-based method for the face recognition system and compare the results with the direct two-dimensional principal component analysis face recognition scheme for makeup datasets. Experimental results show the highest accuracy rate of 97.07% was achieved by the proposed algorithm for face recognition system considering makeup.

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.Growth stage (GS) is an important crop growth metric commonly used in commercial farms. We focus on wheat and barley GS classification based on in-field proximal images using convolutional neural networks (ConvNets). For comparison purposes, use of a conventional machine learning algorithm was also investigated. The research includes extensive data collection of images of wheat and barley crops over a 3-year period. During data collection, videos were recorded during field walks at two camera views: downward looking and 45 deg angled. The resulting dataset contains 110,000 images of wheat and 106,000 of barley taken over 34 and 33 GS classes, respectively. Three methods were investigated as candidate technologies for the problem of GS classification. These methods were: (I) feature extraction and support vector machine, (II) ConvNet with learning from scratch, and (III) ConvNet with transfer learning. The methods were assessed for classification accuracy using test images taken (a) in fields on days imagined in the training data (i.e., seen field-days GS classification) and (b) in fields on days not imagined in the training data (i.e., unseen field-days principal GS classification). Of the three methods investigated, method III achieved the best accuracy for both classification tasks. The model achieved 97.3% and 97.5% GS classification accuracy for seen field-day test data for wheat and barley, respectively. The model also achieved accuracies of 93.5% and 92.2% for the principal GS classification task for wheat and barley, respectively. We provide a number of key research contributions: the collection curation and exposure of a unique GS labeled proximal image dataset of wheat and barley crops, GS classification, and principal GS classification of cereal crops using three different machine learning methods as well as a comprehensive evaluation and comparison of the obtained results.