Reliability of camera systems to recognize facial features for access to specialized production areas

The article deals with ergonomics and reliability of camera systems for recognition of facial features and identify person for access to specialized areas. The monitoring of areas relates not only to crime, but it is also an integral part of access to specialized production areas (pharmaceutical production, chemical production, specialized food production, etc.). It is therefore important to adequately secure these premises using the relevant system. One of them is a system based on user identification using specific facial features. For this purpose, there are CCTV systems for recognition of facial features of different price categories (conventional cameras, semi-professional and professional) on the world market. However, problematic situations may occur when identifying. For example, by having the user partially masked face. This research is focusing on the problem. The main goal of the research is establishing the scale of negative impact, in case the identified person has partially masked face, on camera systems recognizing facial features, primarily on recognition time. The results are evaluated in detail. Some camera systems are not suitable in specialized production areas due to their insufficient recognition ability. From all the tested devices, the HIKVISION iDS-2CD8426G0 / F-I camera identification system has proved to be optimal for identification purposes. In the case of designing, it is therefore necessary to choose suitable camera systems that have ergonomics and reliability at a level that will guarantee their sufficient use in the mentioned areas, while decreasing comfort and user-friendliness as little as possible. By measuring the ergonomics and reliability of these CCTV systems, it can be stated that there are statistically significant differences between conventional, semi-professional and professional systems, and it’s not just a design change, but also a more efficient recognition method

Speaker identification and clustering using convolutional neural networks

Deep learning, especially in the form of convolutional neural networks (CNNs), has triggered substantial improvements in computer vision and related fields in recent years. This progress is attributed to the shift from designing features and subsequent individual sub-systems towards learning features and recognition systems end to end from nearly unprocessed data. For speaker clustering, however, it is still common to use handcrafted processing chains such as MFCC features and GMM-based models. In this paper, we use simple spectrograms as input to a CNN and study the optimal design of those networks for speaker identification and clustering. Furthermore, we elaborate on the question how to transfer a network, trained for speaker identification, to speaker clustering. We demonstrate our approach on the well known TIMIT dataset, achieving results comparable with the state of the art – without the need for handcrafted features

Quantum learning: optimal classification of qubit states

Pattern recognition is a central topic in Learning Theory with numerous applications such as voice and text recognition, image analysis, computer diagnosis. The statistical set-up in classification is the following: we are given an i.i.d. training set $(X_{1},Y_{1}),... (X_{n},Y_{n})$ where $X_{i}$ represents a feature and $Y_{i}\in \{0,1\}$ is a label attached to that feature. The underlying joint distribution of $(X,Y)$ is unknown, but we can learn about it from the training set and we aim at devising low error classifiers $f:X\to Y$ used to predict the label of new incoming features. Here we solve a quantum analogue of this problem, namely the classification of two arbitrary unknown qubit states. Given a number of `training' copies from each of the states, we would like to `learn' about them by performing a measurement on the training set. The outcome is then used to design mesurements for the classification of future systems with unknown labels. We find the asymptotically optimal classification strategy and show that typically, it performs strictly better than a plug-in strategy based on state estimation. The figure of merit is the excess risk which is the difference between the probability of error and the probability of error of the optimal measurement when the states are known, that is the Helstrom measurement. We show that the excess risk has rate $n^{-1}$ and compute the exact constant of the rate.Comment: 24 pages, 4 figure

Condition monitoring of helical gears using automated selection of features and sensors

The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or red undant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications

Human inspired pattern recognition via local invariant features

Vision is increasingly becoming a vital element in the manufacturing industry. As complex as it already is, vision is becoming even more difficult to implement in a pattern recognition environment as it converges toward the level of what humans visualize. Relevant brain work technologies are allowing vision systems to add capability and tasks that were long reserved for humans. The ability to recognize patterns like humans do is a good goal in terms of performance metrics for manufacturing activities. To achieve this goal, we created a neural network that achieves pattern recognition analogous to the human visual cortex using high quality keypoints by optimizing the scale space and pairing keypoints with edges as input into the model. This research uses the Taguchi Design of Experiments approach to find optimal values for the SIFT parameters with respect to finding correct matches between images that vary in rotation and scale. The approach used the Taguchi L18 matrix to determine the optimal parameter set. The performance obtained from SIFT using the optimal solution was compared with the performance from the original SIFT algorithm parameters. It is shown that correct matches between an original image and a scaled, rotated, or scaled and rotated version of that image improves by 17% using the optimal values of the SIFT. A human inspired approach was used to create a CMAC based neural network capable of pattern recognition. A comparison of 3 object, 30 object, and 50 object scenes were examined using edge and optimized SIFT based features as inputs and produced extensible results from 3 to 50 objects based on classification performance. The classification results prove that we achieve a high level of pattern recognition that ranged from 96.1% to 100% for objects under consideration. The result is a pattern recognition model capable of locally based classification based on invariant information without the need for sets of information that include input sensory data that is not necessarily invariant (background data, raw pixel data, viewpoint angles) that global models rely on in pattern recognition

Impact of feature proportion on matching performance of multi-biometric systems

Biometrics as a tool for information security has been used in various applications. Feature-level fusion is widely used in the design of multi-biometric systems due to its advantages in increasing recognition accuracy and security. However, most existing multi-biometric systems that use feature-level fusion assign each biometric trait an equal proportion when combining features from multiple sources. For example, multi-biometric systems with two biometric traits commonly adopt a 50–50 feature proportion setting, which means that fused feature data contains half elements from each biometric modality. In this paper, we investigate the impact of feature proportion on the matching performance of multi-biometric systems. By using a fingerprint and face based multi-biometric system that applies feature-level fusion, we employ a random projection based transformation and a proportion weight factor. By adjusting this weight factor, we show that allocating unequal proportions to features from different biometric traits yields different matching performance. Our experimental results indicate that optimal performance, achieved with unequal feature proportions, could be better than the performance obtained with the commonly used 50–50 feature proportion. Therefore, the impact of feature proportion, which has been ignored by most existing work, should be taken into account and more study is required as to how to make feature proportion allocation benefit the performance of multi-biometric systems

Marking and making : a characterisation of sketching for typographic design

This research rests on the premise that sketching in paper and pencil is crucial for typographic designers when designing documents. The aim has been to derive a characterisation of the salient aspects of sketching, through an ethnographically-oriented study of designers' use of paper and pencil. The people studied were professional typographic designers, but both the motivations for the research and the characterisation deriving from it relate to other design disciplines, notably industrial and engineering design and architecture. The goal was to identify the underlying functionality supported by sketching, in order to inform the design of future tools for document creation. The characterisation is presented as a framework, with seven main categories: visual characteristics of marks; basic semantic units of design; visual features of sketches; visual and tactile features of sheets of sketches; affordances of sketching; functionality required to support sketching; capacities of the traditional medium. The first four categories deal with the visual qualities of sketches, such as image quality within the line and recurring features in sketches such as different scale, closure, and degree of detail. The functions supported by sketching are suggested to be: interpretability, focus, comparison, simulation of experience, ideas capture and record making. The functionality identified as necessary to support sketching includes the appropriate speed of image generation, image emergence, image manipulation, and image capture and record making. Also necessary are high speed and ease of switching between all the strands mentioned above, and singularity of focus. The supportive capacities of the traditional medium include a rich vocabulary of marks, high image definition, and the continuum-of-activity through the continuity-at-medium, i.e. the natural progression from sketching on paper to making simulations out of paper. In recognition of the respective strengths of the traditional and electronic media, integration between the two is recommended for the design of optimal document creation systems

Smart automated computer-aided process planning (ACAPP) for rotational parts based on feature technology and STEP file

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe concept of smart manufacturing comprises high levels of adaptability with rapid design changes, digital information technology, and more data training. This differs from traditional manufacturing, which depends on constant inputs for the generation of process planning to manufacture a part or requires human intervention if any of the input changes. Smart manufacturing has become a vital issue in the manufacturing industry since the start of the twenty-first century, in terms of time and production cost. One of the most effective concepts for achieving a smart manufacturing industry is the use of Computer-Aided Process Planning (CAPP) which is the key technology that connects Computer-Aided Design (CAD) and the Computer-Aided Manufacturing (CAM) processes. A lot of effort has been spent taking CAPP systems to the next upgraded level that is Automated Computer- Aided Process Planning (ACAPP) in order to provide complete information about the product, in a way that is automated, fast, and accurate. One of the most import aspects in creating an ACAPP system is the use of feature technology, as it is the first step in converting the design to manufacturing features. This includes in particular the development of efficient Automatic Feature Recognition (AFR) systems and solving features intersecting issues. The implementation of AFR techniques is an indispensable concept for transferring product data between CAD and ACAPP systems. Different international Product Data Exchange (PDE) standards, such as Drawing Exchange Format (DXF), Initial Graphic Exchange Specification (IGES), and Standard for the Exchange of Product (STEP) files are used to accomplish this purpose. Although many AFR techniques and systems have been developed to serve this aim, each of them has limitations. For example, each system is restricted to recognise a specific set of predefined manufacturing features; hence, if new features are included in the model design, they will not be recognised. In this work, a novel and smart interactive AFR (SI-AFR) system has been proposed for recognising features of rotational parts. A parser has been developed to extract the geometrical and topological information of a part design from a STEP file and to send it to the next steps. Then, the system manipulates the extracted information to facilitate the feature recognition process. During this progression, the system contributes to solving issues considered drawbacks in previous works, such as identifying the convexity and concavity of toroidal surfaces and efficiently isolating faces that belong to holes and internal shapes. Finally, the feature recognition process has been divided into two parts: recognition of predefined features and smart interactive feature recognition. This has been written using C# coding to extract the features’ geometrical and topological information from the STEP file. Whilst the first part of the proposed system has the ability of recognising 54 predefined features, the main contribution of this research is concentrated in the second part of the system which allows new features to be detected, identified, and added to the predefined feature set. This is achieved by extracting the type and specification of each face, the geometrical and topological relation between each two adjacent faces, and the number of the faces that form the new feature. Due to its ability in identifying predefined and new features, it is believed that the system represents a new generation of feature recognition systems. Also, a “features subtraction” system has been created as an optimal solution for complex features intersecting cases. It takes the final manufacturing features from the SI-AFR system as an input. The system has seven steps for analysing, processing, and calculating intermediate features. The intermediate features represent layers of material to be removed, in an optimal sequence. These are recognised by scanning in all directions of the part, to determine the intersecting areas between the final manufacturing features. Such a system provides a whole vision of transferring a blank into the desired shape via step-by-step rough turning, drilling, and boring processes. The results from the SI-AFR and features subtraction systems depend on the geometrical and topological information of the pre-defined and new features. These are analysed for the purpose of automatically generating CAPP outputs, such as the process selection, cutting tools, sequence of operations, and generating G-code. This is to reduce the time and production cost, as well as human intervention, and hence significantly contributes to an organisations efforts in sustainability. The proposed ACAPP system has been practically validated, clearly demonstrating how it surpasses the capabilities of traditional CAM software, since all the outputs are achieved automatically, which CAM software are currently not capable of. The final manufacturing features of the part have been produced accurately, compared to the design features, in terms of specified design dimensions and tolerances. The current version of the system covers rotational symmetrical parts, however this work can be extended to include rotational non-symmetrical and prismatic parts.Republic of Iraq Ministry of Higher Education & Scientific Researc

Application of multiobjective genetic programming to the design of robot failure recognition systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge