HP2IFS: Head Pose estimation exploiting Partitioned Iterated Function Systems

Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches

Web-Shaped Model for Head Pose Estimation: an Approach for Best Exemplar Selection

Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. In this respect, the selection of the frames with the best face orientation can allow triggering recognition only on these, therefore decreasing the possibility of errors. This paper proposes a novel approach to head pose estimation for smart cities and video surveillance scenarios, aiming at this goal. The method relies on a cascade of two models: the first one predicts the positions of 68 well-known face landmarks; the second one applies a web-shaped model over the detected landmarks, to associate each of them to a specific face sector. The method can work on detected faces at a reasonable distance and with a resolution that is supported by several present devices. Results of experiments executed over some classical pose estimation benchmarks, namely Point '04, Biwi, and AFLW datasets show good performance in terms of both pose estimation and computing time. Further results refer to noisy images that are typical of the addressed settings. Finally, examples demonstrate the selection of the best frames from videos captured in video surveillance conditions

Near real-time three axis head pose estimation without training

n/

RFCNN: Traffic Accident Severity Prediction Based on Decision Level Fusion of Machine and Deep Learning Model

Traffic accidents on highways are a leading cause of death despite the development of traffic safety measures. The burden of casualties and damage caused by road accidents is very high for developing countries. Many factors are associated with traffic accidents, some of which are more significant than others in determining the severity of accidents. Data mining techniques can help in predicting influential factors related to crash severity. In this study, significant factors that are strongly correlated with the accident severity on highways are identified by Random Forest. Top features affecting accidental severity include distance, temperature, wind_Chill, humidity, visibility, and wind direction. This study presents an ensemble of machine learning and deep learning models by combining Random Forest and Convolutional Neural Network called RFCNN for the prediction of road accident severity. The performance of the proposed approach is compared with several base learner classifiers. The data used in the analysis include accident records of the USA from February 2016 to June 2020. Obtained results demonstrate that the RFCNN enhanced the decision-making process and outperformed other models with 0.991 accuracy, 0.974 precision, 0.986 recall, and 0.980 F-score using the 20 most significant features in predicting the severity of accidents

Periocular Data Fusion for Age and Gender Classification

In recent years, the study of soft biometrics has gained increasing interest in the security and business sectors. These characteristics provide limited biometric information about the individual; hence, it is possible to increase performance by combining numerous data sources to overcome the accuracy limitations of a single trait. In this research, we provide a study on the fusion of periocular features taken from pupils, fixations, and blinks to achieve a demographic classification, i.e., by age and gender. A data fusion approach is implemented for this purpose. To build a trust evaluation of the selected biometric traits, we first employ a concatenation scheme for fusion at the feature level and, at the score level, transformation and classifier-based score fusion approaches (e.g., weighted sum, weighted product, Bayesian rule, etc.). Data fusion enables improved performance and the synthesis of acquired information, as well as its secure storage and protection of the multi-biometric system's original biometric models. The combination of these soft biometrics characteristics combines flawlessly the need to protect individual privacy and to have a strong discriminatory element. The results are quite encouraging, with an age classification accuracy of 84.45% and a gender classification accuracy of 84.62%, respectively. The results obtained encourage the studies on periocular area to detect soft biometrics to be applied when the lower part of the face is not visible

L'image du mois : une parathyroïde ectopique

peer reviewe

FASHE: A FrActal based Strategy for Head pose Estimation

Head pose estimation (HPE) represents a topic central to many relevant research fields and characterized by a wide application range. In particular, HPE performed using a singular RGB frame is particular suitable to be applied at best-frame-selection problems. This explains a growing interest witnessed by a large number of contributions, most of which exploit deep learning architectures and require extensive training sessions to achieve accuracy and robustness in estimating head rotations on three axes. However, methods alternative to machine learning approaches could be capable of similar if not better performance. To this regard, we present FASHE, an approach based on partitioned iterated function systems (PIFS) to represent auto-similarities within face image through a contractive affine function transforming the domain blocks extracted only once by a single frontal reference image, in a good approximation of the range blocks which the target image has been partitioned into. Pose estimation is achieved by finding the closest match between fractal code of target image and a reference array by means of Hamming distance. The results of experiments conducted exceed the state of the art on both Biwi and Ponting'04 datasets as well as approaching those of the best performing methods on the challenging AFLW2000 database. In addition, the applications to GOTCHA Video Dataset demonstrate that FASHE successfully operates in-the-wild