Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

Hybrid Approach Based on Machine Learning for Hand Shape and Key Point’s Estimation

In human-computer interaction and virtual truth, hand pose estimation is essential. Public dataset experimental analysis Different biometric shows that a particular system creates low manual estimation errors and has a more significant opportunity for new hand pose estimation activity. Due to the fluctuations, self-occlusion, and specific modulations, the structure of hand photographs is quite tricky. Hence, this paper proposes a Hybrid approach based on machine learning (HABoML) to enhance the current competitiveness, performance experience, experimental hand shape, and key point estimation analysis. In terms of strengthening the ability to make better self-occlusion adjustments and special handshake and poses estimations, the machine learning algorithm is combined with a hybrid approach. The experiment results helped define a set of follow-up experiments for the proposed systems in this field, which had a high efficiency and performance level. The HABoML strategy decreased analysis precision by 9.33% and is a better solution.

PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging

Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.

Design and Development of Zero Turn Material Handling Robot

This paper aims to a zero turn material handling robot driven by pneumatic actuator as versatile end effectors for the material handling system. The arm consists of pneumatic hand and pneumatic wrist. The arm can grasp various objects without force sensors or feedback control of any. Therefore, this study aims to control wrist motion space. Hand shape is similar to the human hand with mechanical characteristic. Althe pneumatic actuators used as the drive source. This system develops the robot having rotary motion independent of the base. This model can be used to overcome the problem of space limitation and reduces the labour cost in small scale industries. It includes the robot arm, pneumatic cylinder and motors. Finally a zero turn robot with pneumatic system is fabricated.

Get a Grip: Variation in Human Hand Grip Strength and Implications for Human Evolution

Although hand grip strength is critical to the daily lives of humans and our arboreal great ape relatives, the human hand has changed in form and function throughout our evolution due to terrestrial bipedalism, tool use, and directional asymmetry (DA) such as handedness. Here we investigate how hand form and function interact in modern humans to gain an insight into our evolutionary past. We measured grip strength in a heterogeneous, cross-sectional sample of human participants (n = 662, 17 to 83 years old) to test the potential effects of age, sex, asymmetry (hand dominance and handedness), hand shape, occupation, and practice of sports and musical instruments that involve the hand(s). We found a significant effect of sex and hand dominance on grip strength, but not of handedness, while hand shape and age had a greater influence on female grip strength. Females were significantly weaker with age, but grip strength in females with large hands was less affected than those with long hands. Frequent engagement in hand sports significantly increased grip strength in the non-dominant hand in both sexes, while only males showed a significant effect of occupation, indicating different patterns of hand dominance asymmetries and hand function. These results improve our understanding of the link between form and function in both hands and offer an insight into the evolution of human laterality and dexterity.

Approach to hand posture recognition based on hand shape features for human–robot interaction

Hand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.