Abstract-Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resourcelimited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice.

Estimating human face gender from images is a problem that has been extensively studied because of its relevant applications. Recent works report significant drops in performance for state-of-the-art gender classifiers when evaluated "in the wild," i.e. with uncontrolled demography and environmental conditions. We hypothesize that this is caused by the existence of dependencies among facial demographic attributes that have not been considered when building the classifier. In the paper we study the dependencies among gender, age and pose facial attributes. By considering the relation between gender and pose attributes we also avoid the use of computationally expensive and fragile face alignment procedures. In the experiments we confirm the existence of dependencies among gender, age and pose facial attributes and prove that we can improve the performance and robustness of gender classifiers by exploiting these dependencies.

The Addenbrooke’s Cognitive Examination III is one of the most widely used tests to assess cognitive impairment. Although previous studies have shown adequate levels of diagnostic utility to detect severe impairment, it has not shown sensitivity to detect mild decline. The aim of this study was to evaluate the psychometric properties of Addenbrooke’s Cognitive Examination III in a large sample of elderly people through Item Response Theory, due to the lack of studies using this approach. A cross-sectional study was conducted with 1164 people from the age of 60 upwards, of which 63 had a prior diagnosis of Alzheimer dementia. The results showed that, globally, the Addenbrooke’s Cognitive Examination III possesses adequate psychometrics properties. Furthermore, the information function test shows that the subscales have different sensitivity to different levels of impairment. These results can contribute to determining patterns of cognitive deterioration for the adequate detection of different levels of dementia. An optimized version is suggested which may be an economic alternative in the applied field.

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