Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons

Abstract

Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

“…Haar-like performs image classification based on simple feature values [26]. This feature is one of the methods of Viola-Jones [27].…”

Haar-features training parameters analysis in boosting based machine learning for improved face detection

“…In [20] authors developed face detectors using Haarlike and local binary pattern features, based on their number of uses at each stage of training, study of training parameters FAR (False alarm rate) and TPR (True positive rate) has been done in MATLAB's trainCascadeObjectDetector function.…”

Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

“…In the Modeling part, we labelled some target patterns in the input image and collected positive and negative pattern pools from these input images. Then, according to the aspect ratio of the target patterns, we generated the Haar-like [22], HOG [26], and LBP [23] initial feature pools. After that, we determine the applicability of a feature to the patterns by calculating the positive and negative average response value.…”

“…The online learning algorithms [14][15][16], use a single feature during learning. If they use a feature that does not detect a certain object well, for example, using a Haar-like feature [22] to detect a ball-shaped object or LBP [23] to detect a smooth object, the performance suffers. In particular, multi-class classification has this problem because a single feature has insufficient distinguishing ability to distinguish several objects at the same time.…”

“…In the pattern pool, the proposed model adopts the same method in On-line Learning with Adaptive Model [24] to make an update. In the feature part, the proposed model uses Haar-like [22], Histogram of Oriented Gradient (HOG) [26], and Local Binary Patterns (LBP) [23]. Except for simplifying the last two features so that it can use Integral image [27] to speed up the model, we also proposed an algorithm to dynamically allocate and replace features according to the learning samples.…”