To date, relatively little research has investigated participants’ ability to monitor their memory for faces and names. Four experiments were conducted with aim of developing a comprehensive profile of memory monitoring performance during face identification tasks. In each experiment, memory monitoring judgements were solicited during encoding and/or retrieval of unfamiliar face-name pairs. In general, subjective estimates of future and past memory performance were valid predictors of objective memory performance, regardless of whether a face or name was the item to be retrieved from memory. As a test of the stability of memory monitoring accuracy across different categories of stimuli, memory monitoring for face-name pairs was compared with noun-noun pairs. The predictive validity of estimates of future memory performance was similar across the categories of stimuli, but the predictive validity of estimates of past memory performance was superior for nouns compared with names. A subset of the studies examined the influence of face and name distinctiveness on memory and memory monitoring for face-name associations. This was done in an attempt to identify sources of information that individuals might use to monitor their memory during face-name learning. The beneficial effects of distinctiveness on associative memory were symmetrical between faces and names, such that relative to their typical counterparts, distinct faces enhanced memory for names, and distinct names enhanced memory for faces. These effects were also apparent in memory monitoring. Estimates of future and past memory performance were greater for face-name associations that contained a distinct face or name compared with a typical face or name, regardless of whether the distinct item was a cue or target. Moreover, the predictive validity of prospective monitoring improved with name distinctiveness, whereas the predictive validity of retrospective monitoring improved with facial distinctiveness. Altogether, the results of the dissertation indicate that participants can monitor their memory for faces and names at a level above chance, that retrospective metamemory is more accurate for nouns compared with names, and that distinctiveness not only affects the strength of the association between a face and a name, but also the ability to monitor that association.

To date, relatively little research has investigated participants’ ability to monitor their memory for faces and names. Four experiments were conducted with aim of developing a comprehensive profile of memory monitoring performance during face identification tasks. In each experiment, memory monitoring judgements were solicited during encoding and/or retrieval of unfamiliar face-name pairs. In general, subjective estimates of future and past memory performance were valid predictors of objective memory performance, regardless of whether a face or name was the item to be retrieved from memory. As a test of the stability of memory monitoring accuracy across different categories of stimuli, memory monitoring for face-name pairs was compared with noun-noun pairs. The predictive validity of estimates of future memory performance was similar across the categories of stimuli, but the predictive validity of estimates of past memory performance was superior for nouns compared with names. A subset of the studies examined the influence of face and name distinctiveness on memory and memory monitoring for face-name associations. This was done in an attempt to identify sources of information that individuals might use to monitor their memory during face-name learning. The beneficial effects of distinctiveness on associative memory were symmetrical between faces and names, such that relative to their typical counterparts, distinct faces enhanced memory for names, and distinct names enhanced memory for faces. These effects were also apparent in memory monitoring. Estimates of future and past memory performance were greater for face-name associations that contained a distinct face or name compared with a typical face or name, regardless of whether the distinct item was a cue or target. Moreover, the predictive validity of prospective monitoring improved with name distinctiveness, whereas the predictive validity of retrospective monitoring improved with facial distinctiveness. Altogether, the results of the dissertation indicate that participants can monitor their memory for faces and names at a level above chance, that retrospective metamemory is more accurate for nouns compared with names, and that distinctiveness not only affects the strength of the association between a face and a name, but also the ability to monitor that association.

To date, relatively little research has investigated participants’ ability to monitor their memory for faces and names. Four experiments were conducted with aim of developing a comprehensive profile of memory monitoring performance during face identification tasks. In each experiment, memory monitoring judgements were solicited during encoding and/or retrieval of unfamiliar face-name pairs. In general, subjective estimates of future and past memory performance were valid predictors of objective memory performance, regardless of whether a face or name was the item to be retrieved from memory. As a test of the stability of memory monitoring accuracy across different categories of stimuli, memory monitoring for face-name pairs was compared with noun-noun pairs. The predictive validity of estimates of future memory performance was similar across the categories of stimuli, but the predictive validity of estimates of past memory performance was superior for nouns compared with names. A subset of the studies examined the influence of face and name distinctiveness on memory and memory monitoring for face-name associations. This was done in an attempt to identify sources of information that individuals might use to monitor their memory during face-name learning. The beneficial effects of distinctiveness on associative memory were symmetrical between faces and names, such that relative to their typical counterparts, distinct faces enhanced memory for names, and distinct names enhanced memory for faces. These effects were also apparent in memory monitoring. Estimates of future and past memory performance were greater for face-name associations that contained a distinct face or name compared with a typical face or name, regardless of whether the distinct item was a cue or target. Moreover, the predictive validity of prospective monitoring improved with name distinctiveness, whereas the predictive validity of retrospective monitoring improved with facial distinctiveness. Altogether, the results of the dissertation indicate that participants can monitor their memory for faces and names at a level above chance, that retrospective metamemory is more accurate for nouns compared with names, and that distinctiveness not only affects the strength of the association between a face and a name, but also the ability to monitor that association.

To date, relatively little research has investigated participants’ ability to monitor their memory for faces and names. Four experiments were conducted with aim of developing a comprehensive profile of memory monitoring performance during face identification tasks. In each experiment, memory monitoring judgements were solicited during encoding and/or retrieval of unfamiliar face-name pairs. In general, subjective estimates of future and past memory performance were valid predictors of objective memory performance, regardless of whether a face or name was the item to be retrieved from memory. As a test of the stability of memory monitoring accuracy across different categories of stimuli, memory monitoring for face-name pairs was compared with noun-noun pairs. The predictive validity of estimates of future memory performance was similar across the categories of stimuli, but the predictive validity of estimates of past memory performance was superior for nouns compared with names. A subset of the studies examined the influence of face and name distinctiveness on memory and memory monitoring for face-name associations. This was done in an attempt to identify sources of information that individuals might use to monitor their memory during face-name learning. The beneficial effects of distinctiveness on associative memory were symmetrical between faces and names, such that relative to their typical counterparts, distinct faces enhanced memory for names, and distinct names enhanced memory for faces. These effects were also apparent in memory monitoring. Estimates of future and past memory performance were greater for face-name associations that contained a distinct face or name compared with a typical face or name, regardless of whether the distinct item was a cue or target. Moreover, the predictive validity of prospective monitoring improved with name distinctiveness, whereas the predictive validity of retrospective monitoring improved with facial distinctiveness. Altogether, the results of the dissertation indicate that participants can monitor their memory for faces and names at a level above chance, that retrospective metamemory is more accurate for nouns compared with names, and that distinctiveness not only affects the strength of the association between a face and a name, but also the ability to monitor that association.

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Background: With the rise of digitalization, there has been an increasing needfor secure and effective identification solutions, particularly in the realm of votingsystems. Facial biometric technology has emerged as a potential solution to combat fraud and improve the transparency and security of the voting process. Two well known facial identification algorithms, Local Binary Pattern Histograms (LBPH) and Eigenfaces, have been extensively used in computer vision for facial identification.However, their effectiveness in the context of a smart voting system is still a matter of debate. Objectives: The aim of this project is to compare the effectiveness of LBPH and Eigenfaces algorithms in the development of a smart voting system using the Haar cascade for face detection. The objective is to identify the more suitable approach between the two algorithms, considering factors such as lighting conditions and the facial expressions of the individuals being identified. The goal is to evaluate the algorithms using various metrics such as accuracy, precision, recall, and F1 score. Methods: The project involves the comparison of facial identification algorithms using the Haar cascade for face detection. Both the LBPH and Eigenfaces algorithms are implemented and evaluated in a complex environment that is similar to a polling station. The algorithms are trained and tested using a dataset of facial images with varying lighting conditions and facial expressions. The evaluation metrics, including accuracy, precision, recall, and F1 score, are used to compare the performance of thetwo algorithms. Results: The results of the project indicate that the LBPH algorithm performs better than Eigenfaces in terms of accuracy and performance. The algorithms havebeen tested with faces and objects in low-light conditions. Their accuracy and performance are also measured. Conclusions: The comparison of LBPH and Eigenfaces algorithms using the Haarcascade for face detection reveals that LBPH is a more suitable approach. The comparison of facial identification-based algorithms can significantly contribute to the voting process, thereby ensuring integrity of the voting process. The findings of this project can contribute to the development of a more reliable and secure voting system, and the evaluation metrics used in this project can be applied to future research in the field of facial identification purposes.

In eyewitness identification cases, suspect misidentification is the leading factor attributed to wrongful convictions (Scheck, Neufeld, & Dwyer, 2000), thus, it is of applied importance to identify factors that contribute to the false recollection of faces. One potential factor addressed in the current study was whether face memory and subsequent identification for other-race-faces is biased by the degree to which a target face posses facial features associated with ethnic identity. Individual differences in level of processing (global, local) and prejudice were tested as potential mechanisms contributing to biased judgments. In Experiment 1 a standard face recognition task revealed that prejudice, level of processing, and face-type interacted to predict recognition bias. In Experiment 2 results showed that positive misidentifications (i.e., choosing an incorrect foil) were more likely when a stereotypical versus non-stereotypical Black actor was witnessed committing the crime. Results are discussed in terms of theoretical and practical implications.

In eyewitness identification cases, suspect misidentification is the leading factor attributed to wrongful convictions (Scheck, Neufeld, & Dwyer, 2000), thus, it is of applied importance to identify factors that contribute to the false recollection of faces. One potential factor addressed in the current study was whether face memory and subsequent identification for other-race-faces is biased by the degree to which a target face posses facial features associated with ethnic identity. Individual differences in level of processing (global, local) and prejudice were tested as potential mechanisms contributing to biased judgments. In Experiment 1 a standard face recognition task revealed that prejudice, level of processing, and face-type interacted to predict recognition bias. In Experiment 2 results showed that positive misidentifications (i.e., choosing an incorrect foil) were more likely when a stereotypical versus non-stereotypical Black actor was witnessed committing the crime. Results are discussed in terms of theoretical and practical implications.

Despite decades of effort in academia and industry, it is not yet possible to build machines that can replicate many seemingly-basic human perceptual abilities. This work focuses on the problem of face identification that most of us effortlessly solve daily. Substantial progress has been made towards the goal of automatically identifying faces under tightly controlled conditions; however, in the domain of unconstrained face images, many challenges remain. We observe that the recent combination of widespread digital photography, inexpensive digital storage and bandwidth, and online social networks has led to the sudden creation of repositories of billions of shared photographs and opened up an important new domain for unconstrained face identification research. Drawing upon the newly-popular phenomenon of “tagging,” we construct some of the first face identification datasets that are intended to model the digital social spheres of online social network members, and we examine various qualitative and quantitative properties of these image sets. The identification datasets we present here include up to 100 individuals, making them comparable to the average size of members’ networks of “friends” on a popular online social network, and each individual is represented by up to 100 face samples that feature significant real-world variation in appearance, expression, and pose. We demonstrate that biologically-inspired visual representations can achieve state-of-the-art face identification performance on our novel frontal and multi-pose face datasets. We also show that the addition of a tree-structured classifier and training set augmentation can enhance accuracy in the multi-pose setting. Finally, we illustrate that the machine-readable “social context” in which shared photos are often embedded can be applied to further boost face identification accuracy. Taken together, our results suggest that accurate automated face identification in vast online shared photo collections is now feasible. / Engineering and Applied Sciences

This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of persons, therefore the emphasis on computational complexity. The programs I used a staged method of PCA, LDA and kernel PCA (KPCA). The first program only works with the PCA method, which has good results with respect to the success and speed of recognition. In the second program to compare methods, which passed the best method for KPCA.