Study of Face Orientation Recognition Based on Neural Network

2018 ◽  
Vol 32 (11) ◽  
pp. 1856015 ◽  
Author(s):  
Suping Li ◽  
Zhanfeng Wang ◽  
Jing Wang
Keyword(s):  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Training Data ◽  
Future Research ◽  
Identification System ◽  
Bp Network ◽  
Feature Vectors ◽  
And Performance ◽  
The Stability

Learning vector quantization (LVQ) network and back-propagation (BP) network are constructed easily making use of MATLAB toolbox on the basis of maintaining the recognition rate. Face images are randomly selected from images set as training data of LVQ network and BP network. LVQ algorithm and BP algorithm are used to train network. The automatic recognition of face orientation is realized when the system obtains convergence network. First, all images are processed by edge detection. Then feature vectors representing position of the eye were extracted from edge detected images. Feature vectors of training set are sent to network to adjust the parameters which ensures the convergence speed and performance of the network. Experimental results show that the constructed LVQ network and BP network can judge face orientation according to feature vectors of input images. Generally, the recognition rate of LVQ network is higher than that of BP network. The LVQ network and BP network are both feasible and effective for face orientation recognition to some extent. The advantage of this work is that the recognition system is efficient and easy to promote. This paper focuses on how to use MATLAB easily to design identification network rather than the complexity of identification system. The future research will focus on the stability and robustness of recognition network.

Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

Plastic Classification with X-ray Absorption Spectroscopy Based on Back Propagation Neural Network

2017 ◽  
Vol 71 (11) ◽  
pp. 2538-2548 ◽  
Author(s):  
Qian Wang ◽  
Xiaomei Wu ◽  
Lingcong Chen ◽  
Zheng Yang ◽  
Zheng Fang
Keyword(s):  
Absorption Spectroscopy ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
X Ray ◽  
Analysis Methods ◽  
Average Recognition Rate ◽  
The Stability ◽  
X Ray Absorption

Currently, spectral analysis methods used in the classification of plastics have limitations that do not apply to opaque plastics or the stability of experimental results is not strong. In this paper, X-ray absorption spectroscopy (XAS) has been applied to classify plastics due to its strong penetrability and stability. Fifteen kinds of plastics are selected as specimens. X-ray, which is excited by a voltage of 60 kV, penetrated these specimens. The spectral data acquired by CdTe X-ray detector are processed by principal component analysis (PCA) and other data analysis methods. Then the back propagation neural networks (BPNN) algorithm is used to classify the processed data. The average recognition rate reached 96.95% and classification results of all types of plastic results were analyzed in detail. It indicates that XAS has the potential to classify plastics and that XAS can be used in some fields such as plastic waste sorting and recycling. At the same time, the technology of XAS, in the future, can also be used to classify more substances.

scholarly journals LPI Radar Waveform Recognition Based on CNN and TPOT

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 725 ◽  
Author(s):  
Jian Wan ◽  
Xin Yu ◽  
Qiang Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Recognition System ◽  
Radar Signal ◽  
Automatic Identification ◽  
Identification System ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Barker Codes ◽  
Intelligence Support

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

scholarly journals Face–Iris Multimodal Biometric Identification System

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 85 ◽  
Author(s):  
Basma Ammour ◽  
Larbi Boubchir ◽  
Toufik Bouden ◽  
Messaoud Ramdani
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Gabor Filter ◽  
Singular Spectrum Analysis ◽  
Recognition Rate ◽  
Evaluation Process ◽  
Relevant Information ◽  
Recognition System ◽  
Identification System ◽  
New Feature

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

scholarly journals UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

2002 ◽  
Vol 14 (01) ◽  
pp. 12-19 ◽  
Author(s):  
DUU-TONG FUH ◽  
CHING-HSING LUO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Disabled Persons ◽  
Single Chip ◽  
Morse Code ◽  
Average Recognition Rate ◽  
Rate Test

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

VISUAL-BASED HUMAN ACTION RECOGNITION ON SMART PHONES BASED ON 2D AND 3D DESCRIPTORS

2012 ◽  
Vol 26 (08) ◽  
pp. 1260009 ◽  
Author(s):  
MARC BOSCH-JORGE ◽  
ANTONIO-JOSÉ SÁNCHEZ-SALMERÓN ◽  
CARLOS RICOLFE-VIALA
Keyword(s):  
Action Recognition ◽  
Human Subjects ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Smart Phones ◽  
Image Representations ◽  
And Performance ◽  
3D Descriptors

The aim of this work is to present a visual-based human action recognition system which is adapted to constrained embedded devices, such as smart phones. Basically, vision-based human action recognition is a combination of feature-tracking, descriptor-extraction and subsequent classification of image representations, with a color-based identification tool to distinguish between multiple human subjects. Simple descriptors sets were evaluated to optimize recognition rate and performance and two dimensional (2D) descriptors were found to be effective. These sets installed on the latest phones can recognize human actions in videos in less than one second with a success rate of over 82%.

scholarly journals Performance Improvement of Face Recognition System using Selective Local Feature Vectors

2013 ◽  
Vol 10 (2) ◽  
pp. 1330-1338
Author(s):  
Vasudha S ◽  
Neelamma K. Patil ◽  
Dr. Lokesh R. Boregowda
Keyword(s):  
Face Recognition ◽  
Performance Improvement ◽  
Recognition Rate ◽  
Recognition System ◽  
Image Splicing ◽  
Feature Vectors ◽  
Face Recognition System ◽  
Prime Concern ◽  
Wide Range ◽  
Recognition Systems

Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is of prime concern. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges. Face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. Proposed method uses a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset of feature vectors for actual recognition.

scholarly journals Continuous kannada speech segmentation and speech recognition based on threshold using MFCC And VQ

2019 ◽  
Vol 9 (6) ◽  
pp. 4684
Author(s):  
Vanajakshi Puttaswamy Gowda ◽  
Mathivanan Murugavelu ◽  
Senthil Kumaran Thangamuthu
Keyword(s):  
Speech Recognition ◽  
Language Processing ◽  
Speech Signal ◽  
Recognition Rate ◽  
Recognition System ◽  
Training Data ◽  
Speech Segmentation ◽  
Significant Feature ◽  
Mel Frequency Cepstral Coefficients ◽  
Simple Method

<p><span>Continuous speech segmentation and its  recognition is playing important role in natural language processing. Continuous context based Kannada speech segmentation depends  on context, grammer and semantics rules present in the kannada language. The significant feature extraction of kannada speech signal  for recognition system is quite exciting for researchers. In this paper proposed method  is  divided into two parts. First part of the method is continuous kannada speech signal segmentation with respect to the context based is carried out  by computing  average short term energy and its spectral centroid coefficients of  the speech signal present in the specified window. The segmented outputs are completely  meaningful  segmentation  for different scenarios with less segmentation error. The second part of the method is speech recognition by extracting less number Mel frequency cepstral coefficients with less  number of codebooks  using vector quantization .In this recognition is completely based on threshold value.This threshold setting is a challenging task however the simple method is used to achieve better recognition rate.The experimental results shows more efficient  and effective segmentation    with high recognition rate for any continuous context based kannada speech signal with different accents for male and female than the existing methods and also used minimal feature dimensions for training data.</span></p>

Stability of Two-Immiscible-Fluid Systems: A Review of Canonical Plane Parallel Flows

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Alireza Mohammadi ◽  
Alexander J. Smits
Keyword(s):  
Fluid Layer ◽  
Future Research ◽  
Immiscible Fluid ◽  
Weakly Nonlinear ◽  
Plane Parallel ◽  
Fluid Systems ◽  
Parallel Flows ◽  
Lower Fluid ◽  
And Performance ◽  
The Stability

A brief review is given on the stability of two-fluid systems. Our interest is primarily driven by drag reduction using superhydrophobic surfaces (SHS) or liquid-infused surfaces (LIS) where the longevity and performance strongly depends on the flow stability. Although the review is limited to immiscible, incompressible, Newtonian fluids with constant properties, the subject is rich in complexity. We focus on three canonical plane parallel flows as part of the general problem: pressure-driven flow, shear-driven flow, and flow down an inclined plane. Based on the linear stability, the flow may become unstable to three modes of instabilities: a Tollmein–Schlichting wave in either the upper fluid layer or the lower fluid layer, and an interfacial mode. These instabilities may be further categorized according to the physical mechanisms that drive them. Particular aspects of weakly nonlinear analyses are also discussed, and some directions for future research are suggested.

scholarly journals Hybrid neural network based on novel audio feature for vehicle type identification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haoze Chen ◽  
Zhijie Zhang
Keyword(s):  
Neural Networks ◽  
Visual Information ◽  
Recognition Rate ◽  
Real Life ◽  
Audio Signal ◽  
Training Data ◽  
Identification System ◽  
The Novel ◽  
Vehicle Information ◽  
Audio Information

AbstractDue to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.

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