Generating One Biometric Feature from Another: Faces from Fingerprints

This study presents a new approach based on artificial neural networks for generating one biometric feature (faces) from another (only fingerprints). An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships. The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features. The parameter settings of the system were achieved with the help of Taguchi experimental design technique. The performance and accuracy of the system have been evaluated with 10-fold cross validation technique using qualitative evaluation metrics in addition to the expanded quantitative evaluation metrics. Consequently, the results were presented on the basis of the combination of these objective and subjective metrics for illustrating the qualitative properties of the proposed methods as well as a quantitative evaluation of their performances. Experimental results have shown that one biometric feature can be determined from another. These results have once more indicated that there is a strong relationship between fingerprints and faces

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

Machine learning for voice recognition

Verbal communication is very important to us humans, but using thisperforming verbal communication to communicateion with machines still faces particular challenges. Therefore, researchers are trying to find ways to make communication with a machine more similar to communicating with other people, for which two systems have been identified: speech and voice recognition. While speech recognition has aimed to become speaker independent, voice recognition focuses on identifying the speaker, by looking at the tone of the voice, which is affected by the physical characteristics of that person. This requires one to identify these unique tonal features, to then train a system with this data. Being able to perform this identification well, would also bring benefit to speech recognition by allowing the system to adjust to the characteristics of that speaker and how he/she produces their sounds

New Generation Indonesian Endemic Cattle Classification: MobileNetV2 and ResNet50

Cattle are an essential source of animal food globally, and each country possesses unique endemic cattle races. However, categorizing cattle, especially in countries like Indonesia with a large cattle population, presents challenges due to costs and subjectivity when using human experts. This research utilizes Computer Vision (CV) for image data classification to address this urgent need for automatic categorization. The main objective of this study is to develop a mobile-friendly model using CV techniques that can accurately detect and classify Indonesian endemic cattle races, such as Limosin, Madura, Pegon, and Simental. To achieve this, an object localization approach is employed to extract multiple features from distinct regions of each cattle image, including the head, ear, horn, and muzzle areas. The authors evaluate two CV algorithms, ResNet50 and MobileNetV2, to assess their performance in cattle race classification. The dataset used is facial photos of 147 cows. The results indicate that ResNet50 outperforms MobileNetV2, achieving a training data accuracy of 83.33% compared to MobileNetV2's 77.08%. Moreover, the validation accuracy of ResNet50 (76.92%) significantly surpasses MobileNetV2's (38.46%). The novel contribution of this research lies in developing a cost-effective and efficient solution for identifying endemic cattle breeds in Indonesia. The mobile-friendly model based on ResNet50 demonstrates superior accuracy, enabling cattle farmers and researchers to categorize cattle races with higher precision, reducing manual effort, and minimizing costs. In conclusion, this research provides a valuable advancement in automatic cattle categorization using CV techniques. By offering a practical and accurate model that considers Indonesia's specific cattle breeding conditions, this study contributes to the sustainable management and conservation of endemic cattle races while enhancing the efficiency of cattle farming practices

Towards automated eyewitness descriptions: describing the face, body and clothing for recognition

A fusion approach to person recognition is presented here outlining the automated recognition of targets from human descriptions of face, body and clothing. Three novel results are highlighted. First, the present work stresses the value of comparative descriptions (he is taller than…) over categorical descriptions (he is tall). Second, it stresses the primacy of the face over body and clothing cues for recognition. Third, the present work unequivocally demonstrates the benefit gained through the combination of cues: recognition from face, body and clothing taken together far outstrips recognition from any of the cues in isolation. Moreover, recognition from body and clothing taken together nearly equals the recognition possible from the face alone. These results are discussed with reference to the intelligent fusion of information within police investigations. However, they also signal a potential new era in which automated descriptions could be provided without the need for human witnesses at all

Human metrology for person classification and recognition

Human metrological features generally refers to geometric measurements extracted from humans, such as height, chest circumference or foot length. Human metrology provides an important soft biometric that can be used in challenging situations, such as person classification and recognition at a distance, where hard biometric traits such as fingerprints and iris information cannot easily be acquired. In this work, we first study the question of predictability and correlation in human metrology. We show that partial or available measurements can be used to predict other missing measurements. We then investigate the use of human metrology for the prediction of other soft biometrics, viz. gender and weight. The experimental results based on our proposed copula-based model suggest that human body metrology contains enough information for reliable prediction of gender and weight. Also, the proposed copula-based technique is observed to reduce the impact of noise on prediction performance. We then study the question of whether face metrology can be exploited for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. The performance of the proposed metrology-based method is compared with that of a state-of-the-art appearance-based method for gender classification. Results on several face databases show that the metrology-based approach resulted in comparable accuracy to that of the appearance-based method. Furthermore, we study the question of person recognition (classification and identification) via whole body metrology. Using CAESAR 1D database as baseline, we simulate intra-class variation with various noise models. The experimental results indicate that given enough number of features, our metrology-based recognition system can have promising performance that is comparable to several recent state-of-the-art recognition systems. We propose a non-parametric feature selection methodology, called adapted k-nearest neighbor estimator, which does not rely on intra-class distribution of the query set. This leads to improved results over other nearest neighbor estimators (as feature selection criteria) for moderate number of features. Finally we quantify the discrimination capability of human metrology, from both individuality and capacity perspectives. Generally, a biometric-based recognition technique relies on an assumption that the given biometric is unique to an individual. However, the validity of this assumption is not yet generally confirmed for most soft biometrics, such as human metrology. In this work, we first develop two schemes that can be used to quantify the individuality of a given soft-biometric system. Then, a Poisson channel model is proposed to analyze the recognition capacity of human metrology. Our study suggests that the performance of such a system depends more on the accuracy of the ground truth or training set