This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.

Thirty local geometrical features extracted from 3D hitman face surfaces have been used to model the face for face recognition. They are the most discriminating ones selected from a set of 86. We have experimented with 420 3D-facial meshes (without texture) of 60 individuals. There are 7 images per subject including views presenting fight rotations and facial expressions. The HK algorithm, based in the signs of the mean and Gaussian curvatures, has been used for region segmentation. Experiments under controlled and non-controlled acquisition conditions, considering pose variations and facial expressions, have been achieved to analyze the robustness of the selected characteristics. Success recognition results of 82.0% and 90.16% were obtained when the images are frontal views with neutral expression using PCA and SVM, respectively. The recognition rates only decrease to 76.2% and 77.9% using PCA and SVM matching schemes respectively, under gesture and light face rotation.

Demographic handwriting‐based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender‐and‐handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand‐crafted ones. Our work also describes the first approach to the combined gender‐and‐handedness prediction, which has not been addressed before by other researchers. Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems. Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.