Geometrical-based approach for robust human image detection

Abstract

In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches.

Analysis of the current state of deepfake techniques-creation and detection methods

“…List of some current deepfake datasets are shown in Table 3. In addition, Figure 7 displays our evaluations of several existing deepfake datasets that vary in terms of release year, data sample size, and total number of distinct individuals [49]- [51]. To present the frame-level AUC scores for each mentioned dataset, six of the most effective state-of-the-art deepfake detection techniques that have been compared in this paper and the obtained results are listed in Table 4.…”

Phonocardiogram Classification Based on Machine Learning with Multiple Sound Features

“…The model contains the required number of random trees utilizing the tree coefficient to achieve the voting model for every single tree. The essential structure block of the RF is the DT (Al-Hazaimeh et al, 2019;Amrehn et al, 2018). The KNN classifier is also included in the model, its calculation depends on comparing test and training attributes.…”

Pedestrian Recognition Based on Multi-Scale Weighted HOG

“…Edge detection can be used in image segmentation, texture analysis and object recognition. There are several operators used to detect image edge, most of which use differential operators to detect to the step variation in grey scale level (Al-Hazaimeh et al, 2018;Binelli et al, 2005;Al-Smadi et al, 2016b). The first order derivative use either 2×2 or 3×3 directional derivative mask as an edge operator like Sobel, Prewitt and Roberts edge operators.…”

“…In (Dalal and Triggs, 2005;Tuzel et al, 2008), template-based approaches were used with a sliding window classifier, which provide a favorable result. Moreover, the presence of background and other rigid objects such as vehicles can be utilized in the detection process to improve the recognition performance (Dikmen et al, 2010;Al-Hazaimeh et al, 2018).…”