From clothing to identity; manual and automatic soft biometrics

Soft biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of soft biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This study proposes a novel set of soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer-vision. Categorical and comparative soft clothing traits are derived and used for identification/re identification either to supplement soft body traits or to be used alone. The automatically- and manually-derived soft clothing biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications

Soft biometrics for subject identification using clothing attributes

Recently, soft biometrics has emerged as a novel attribute-based person description for identification. It is likely that soft biometrics can be deployed where other biometrics cannot, and have stronger invariance properties than vision-based biometrics, such as invariance to illumination and contrast. Previously, a variety of bodily soft biometrics has been used for identifying people. Describing a person by their clothing properties is a natural task performed by people. As yet, clothing descriptions have attracted little attention for identification purposes. There has been some usage of clothing attributes to augment biometric description, but a detailed description has yet to be used. We show here how clothing traits can be exploited for identification purposes. We explore the validity and usability of a set of proposed semantic attributes. Human identification is performed, evaluated and compared using different proposed forms of soft clothing traits in addition and in isolation

Color face recognition using quaternion PCA

Recently, biometric systems have attracted the attention of both academic and industrial communities. Advances in hardware and software technologies have paved the way to such growing interest. Nowadays, efficient and cost-effective biometric solutions are continuously emerging. Fingerprint-based biometric systems have emerged as pioneering commercial applications of biometric systems. Face and iris traits have proven to be reliable candidates. Until recently, face recognition research literally followed the research undertaken in the field of fingerprint recognition which is inherently gray-scale. In this paper, efforts are restricted to the investigation of face representations in the color domain. The concept of principal component analysis (PCA) is carried over into the hypercomplex domain (i.e., quaternionic) to define quaternionic PCA (Q-PCA) where color faces are compactly represented. Unlike the existing approaches for handling the color information, the proposed algorithm implicitly accounts for the correlation that exists between the face color components (i.e., red, green and blue, respectively).<br/

Analysing soft clothing biometrics for retrieval

Soft biometrics continues to attract research interest. Traditional body and face soft biometrics have been the main research focus and have been proven, by many researchers, to be usable for identification and retrieval. Also, soft biometrics have been shown to provide several advantages over classic biometrics, such as invariance to illumination and contrast. Other than body and face, little attention has focussed on semantic descriptions of an individual, including clothing attributes. Research has yet to concern clothing characteristics as a major or complementary set of biometric traits. In this paper, we analyse the reliability and significance of clothing information for retrieval purposes. We investigate and rate the viability of semantic clothing descriptions to retrieve a subject correctly, given a verbal description of their clothing

Soft biometrics using clothing attributes for human identification

Recently, soft biometrics has emerged as a novel attribute-based person description for identification. It is likely that soft biometrics can be deployed where other biometrics cannot, and have stronger invariance properties than traditional vision-based biometrics, such as invariance to illumination and contrast. Previously, a variety of soft body and face biometrics have been used for identifying people and have increasingly garnered more research interest and are often considered as major cues for identity, especially in the absence of valid traditional hard biometrics, as in surveillance.Describing a person by their clothing properties is a natural task performed by people. As yet, clothing descriptions have attracted little attention for biometric purposes as it has been considered unlikely to be a potential cue to identity. There has been some usage of clothing attributes to augment biometric description, but a detailed description has yet to be used. In everyday life, several cases and incidents arise highlighting the usefulness and capability of information deduced from clothing regarding identity. Clothing is inherently more effective for short-term identification, since people can change clothes.This thesis introduces semantic clothing attributes as a new form of soft biometrics. The usability and efficacy of a novel set of proposed soft clothing traits is explored, showing how they can be exploited for human identification and re-identification purposes. Furthermore, the viability of these traits is investigated in correctly retrieving a subject of interest, given a verbal description of their clothing. The capability of clothing information is further examined in more realistic scenarios offering viewpoint invariant subject retrieval.Although clothing traits can be naturally described or compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This thesis proposes further a novel set of automatic clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way, we can explore the capability of clothing attributes inferred by human vis-a-vis those which are inferred automatically by computer-vision.Extended analysis of clothing information is conducted. Human identification and retrieval are achieved, evaluated, and compared using different proposed forms of soft clothing biometrics in addition and in isolation. The experimental results of identification and retrieval highlight clothing attributes as a potentially valuable addition to the field of soft biometrics

An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

The article introduces Non-Orthogonal Multiple Access (NOMA) and Filter Bank Multicarrier (FBMC), known as hybrid waveform (NOMAFBMC), as two of the most deserving contenders for fifth-generation (5G) network. High spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid NOMA-FBMC. We compare the spectral efficiency of Orthogonal Frequency DivisionMultiplexing (OFDM), FBMC, NOMA, andNOMA-FBMC. It is seen that the hybrid waveformoutperforms the existing waveforms. Peak to Average Power Ratio (PAPR) is regarded as a significant issue in multicarrier waveforms. The combination of Selective Mapping-Partial Transmit Sequence (SLM-PTS) is an effective way to minimize large peak power inclination. The SLM, PTS, and SLM-PTS procedures are applied to the NOMA-FBMC waveform. This hybrid structure is applied to the existing waveforms. Further, the correlated factors like Bit Error Rate (BER) and Computational Overhead (CO) are studied and computed for these waveforms. The outcome of the work reveals that the NOMA-FBMC waveform coupled with the SLM-PTS algorithm offers superior performance as compared to the prevailing systems

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented in this paper is focused on the multi-class classification of breast cancer. The deep transfer learning approach has been utilized to train the deep learning models, and a pre-processing technique has been used to improve the quality of the ultrasound dataset. The proposed technique utilizes two deep learning models, Mobile- NetV2 and DenseNet201, for the composition of the deep ensemble model. Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results. Subsequently, entropy-based feature selection is used. Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%, while the sensitivity and F1 score were 96.87% and 96.76%, respectively. The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented in this paper is focused on the multi-class classification of breast cancer. The deep transfer learning approach has been utilized to train the deep learning models, and a pre-processing technique has been used to improve the quality of the ultrasound dataset. The proposed technique utilizes two deep learning models, Mobile- NetV2 and DenseNet201, for the composition of the deep ensemble model. Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results. Subsequently, entropy-based feature selection is used. Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%, while the sensitivity and F1 score were 96.87% and 96.76%, respectively. The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature

A Detailed Research on Human Health Monitoring System Based on Internet of Things

The technological advent in smart sensing devices and the Internet has provided practical solutions in various sectors of networking, public and private sector industries, and government organizations worldwide. This study intends to combine the Internet of Things (IoT) technology with health monitoring to make it personalized and timely through allowing the interconnection between the devices. This work is aimed at exploring various wearable health monitoring modules that people wear to monitor heart rate, blood pressure, pulse, body temperature, and physiological information. The information is acquired using the wireless sensor to create a health monitoring system. The data is integrated using the Internet of Things for processing, connecting, and computing to achieve real-time monitoring. The temperature of three people measured by the temperature thermometer is 36.4, 36.7, and 36.5 (°C), respectively, and the average acquired by the monitoring system of the three people is 36.5, 36.4, and 36.5 (°C), respectively, indicating that the system demonstrated relatively accurate and stable testability. The user’s ECG is displayed clearly and conveniently using the ECG acquisition system. The pulse rate of the three people tested by the system is 78, 78, and 79 (times/min), respectively, similar to the medical pulse meter results. The physiological information acquired using the semantic recognition, matching system, and character matching system is relatively accurate. It concludes that the human health monitoring system based on the Internet of Things can provide people with daily health management, instrumental in heightening health service quality and level