Paweł Karczmarek

Local descriptors in application to the aging problem in face recognition

Local descriptors are widely used in face recognition due to their robustness to changes in expression or occlusion in facial images. In this paper, a comparison of local descriptors commonly used in face recognition methods is presented in the context of age changes of individuals. We quantify abilities of local descriptors used in face recognition in the context of age discrimination. The performance of the descriptors is evaluated by experimenting with the FG-NET database. We present the results for different age groups and for various age differences of individuals present in the training and testing images. The values of recognition accuracy are reported in combination with various similarity measures used for classification purposes. Moreover, the performance of the descriptors combined with Gabor wavelet images is tested.

A study in facial features saliency in face recognition: an analytic hierarchy process approach

In this study, we develop a process of estimation of importance of features considered in face recognition by making use of the analytic hierarchy process (AHP). The AHP method of pairwise comparisons realized at three levels of hierarchy becomes crucial to realize a comprehensive weighting of cues so that sound estimates of weights associated with the individual features of faces can be formed. We demonstrate how to carry out an efficient process of face description by using a collection of linguistic descriptors of the features and their groups. Numerical dependencies between the features are quantified with the help of experienced criminology and psychology experts. Finally, we present an entropy-based method of evaluation of the relevance of the estimation process completed by the individuals. The intuitively appealing results of experiments are presented and analyzed in detail.

An application of chain code-based local descriptor and its extension to face recognition

Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstrate that the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm. An extension of Chain Code-Based Local Descriptor (CCBLD) is proposed.CCBLD is applied to face recognition task.Bag-of-Visual-Words paradigm is realized through the dictionary of chain-codes.Test results show that CCBLD is comparable or outperforms other local descriptors.The approach is tested using CAS-PEAL, ColorFERET, FG-NET, and other datasets.

Linguistic descriptors in face recognition: A literature survey and the perspectives of future development

People are highly efficient in recognizing faces. However, it is almost impossible for them to cope with huge datasets of facial images without any computational support. On the other hand, the way people describe the facial features using quite commonly encountered descriptors such as “long nose”, “small eyes” and also allude to their feelings according to a specific person like “seems to be nice”, may be utilized to enhance automatic face recognition systems. This offers an interesting possibility to incorporate human perception of faces and relations between facial features into machine-made computations. To address this aspect, one can engage the linguistic descriptors and the linguistic modeling. In this study, we present a comprehensive survey of the state-of-the-art studies and elaborate on some promising perspectives of the developments in this area.

Generalized Choquet Integral for Face Recognition

In this study, we introduce a recent multicriteria decision theory concept of a new, generalized form of Choquet integral function and its application, in particular to the problem of face classification based on the aggregation of classifiers. Such function may be constructed by a simple replacement of the product used under the Choquet integral sign by any t-norm. This idea brings forward a broad class of aggregation operators, which can be incorporated into the decision-making theory. In this context, in a series of experiments we compare the most known t-norms and thoroughly examine their performance in the process of combining individual classifiers based either on facial regions or classic face recognition methods. Such kind of generalization can successfully improve the classification process provided that the parameters of the t-norms are carefully adjusted.

Chain Code-Based Local Descriptor for Face Recognition

Local descriptors have been one of the most intensively examined mechanisms of image analysis. In this paper, we propose a new chain code-based local descriptor. Unlike many other descriptors existing in the literature, this descriptor is based on string values, which are obtained when starting from a particular point of the image and searching for extrema in a given neighborhood and memorizing a path being traversed through the consequent pixels of the image. We demonstrate that this approach is efficient and helps us preserve both local and global properties of the object. To compare the words we apply the Levenshtein distance. Moreover, four similarity measures (correlation, histogram intersection, chi-square, and Hellinger) are used to compare the histograms of words in the process of classification.

An Evaluation of Fuzzy Measure for Face Recognition

In this paper, we analyze the properties and performance of the Choquet integral and fuzzy measure, particularly \(\lambda \)–fuzzy measure in the context of an aggregation of classifiers based on various facial areas. The fuzzy measure and Choquet integral have been shown to be an efficient aggregation techniques. However, in practice reported so far, the choice of the initial values of the measure corresponding to the saliency of facial features has been dependent upon the expert decision. Here, we propose an algorithmic way of finding these values. For this purpose a Particle Swarm Optimization (PSO) method is considered. The reported experimental results show that the method is more effective than the expert – centered approach.

Utility functions as aggregation functions in face recognition

Face recognition by computers in recent years has been a topic of intensive studies. In this problem, we witness several challenges: one has to cope with large data sets, solve problems of data extraction, and deal with poor quality of images caused by e.g., poor lighting of the subject. There have been a lot of algorithms and classifiers developed, which are aimed at recognizing faces of individuals. In this paper, we present a novel classification method, which involves a collection of classifiers with a certain utility function regarded as an aggregation operator. The nearest neighbor method with various similarity measures is used as a generic classifier for selected face areas. The main task is to assign photos of a person to one of the classes of image present in the available database. This problem is similar to the decision-making process with some evident analogies. If in face recognition, a single classifier is being used, the problem becomes similar to the one of decision-making with a single criterion. When having several classifiers, the problem resembles a problem of a multi-criteria decision making. The second scenario requires an aggregation of the results produced by different classifiers. The paper presents the use of the utility function which is well-known in the decision-making theory as an aggregation operator applied to the results of various classifiers. The study is focused on the two-factor utility function and its variants.

Face recognition by humans performed on basis of linguistic descriptors and neural networks

In this study, we present a new approach to the problem of face classification, which relies on the linguistic description of the facial features. In this method, face descriptors are represented through the Analytic Hierarchy Process (AHP) and formalized as information granules. Moreover, neural networks are used to construct efficient classifiers. Furthermore, with usage of AHP we realize a transition from the linguistic description of the facial features to the vectors of numbers that are used by a neural network in the process of matching faces. The results of experiments demonstrate the potential applicability of our proposal to the forensic investigations. Finally, discussed are important aspects of constructing neural networks regarded as a vehicle to perform classification process.

Linguistic Descriptors and Analytic Hierarchy Process in Face Recognition Realized by Humans

In this paper, we discuss an application of the linguistic descriptions obtained directly from experts’ and treated as the votes when characterizing facial images to carry out face classification. Despite various automated face recognition techniques, the expert’s opinion plays a pivotal role in making classification decisions when recognizing faces, say in problems of suspect identification. Here, we analyze the impact of critical factors (e.g., a number of experts, voting schemes, distance functions) and their impact on the performance of classification schemes. The well-established Analytic Hierarchy Process (AHP) is used to quantify importance of linguistic descriptors in the process of face recognition by humans. As a result we produce realistic weights improving the accuracy of classification. Experimental results are presented including a number of parametric studies.