Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Abstract

Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD), Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

“…the relationship with previous research on ML is related In the field of network security, machine learning (ML) has been utilized to help detect SQL injection security attacks. Several ML methods including KNN and NB have been used for this purpose [4] [5].…”

“…To comprehensively evaluate the model's performance, the researchers implemented eight different scenarios for splitting the data, encompassing ratios of [0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]. The grid search was conducted over a range of parameters, including the degree parameter [2,3,4,5,6], C values [0.1, 1, 10, 100, 1000], and coef0 values [0.0, 0.1, 0.5, 1.0].The evaluation of the model's performance was conducted through various metrics, including confusion matrix metrics such as precision (Pre), recall (Rec), F1score (F1-scr), and accuracy (Acc). These metrics are based on the outcomes of TP, TN, FP, and FN.In summary, the study encompasses a comprehensive analysis of sentiment classification in Indonesian online marketplace reviews, considering different data split scenarios and employing SVM with linear and non-linear kernels.…”