Unconstrained Minimum Average Correlation Energy Research Articles

Genre Classification of Traditional Malay Music Using Spectrogram Correlation

A method to classify the genre of traditional Malay music using spectrogram correlation is described. The method can be divided into three distinct parts consisting of spectrogram construction that retains the most salient feature of the music, template construction that takes into account the variations in music within a genre as well as the music progresses, and template matching based on spectrogram image cross-correlation with unconstrained minimum average correlation energy filters. Experiments conducted with seven genres of traditional Malay music show that the recognition accuracy is dependent on the number of segments used to construct the filter templates, which in turn is related to the length of music segment used. Despite using a small dataset, an average recognition rate of 61.8 percent was obtained for music segments lasting 180 seconds using six relatively short excerpts.

Robust face recognition via non‐linear correlation filter bank

The author proposes a robust face recognition algorithm called non-linear correlation filter bank (NCFB). NCFB combines the benefits of a sigmoid function such as non-linearities of image pixels and correlation filters (CFs) to achieve better recognition performance. The sigmoid function is used in the spatial domain to extend the uniform dynamic range for enhancing the image contrast. Greyscale images are divided into non-overlapping regions. CFs are designed based on the unconstrained minimum average correlation energy corresponding to each sub-region of images to optimise the overall correlation outputs. NCFB not only takes the differences among face sub-regions into account but also effectively exploits the discriminative information in face sub-regions. The author shows that the proposed method is robust against illumination, pose, and facial expression variations. Experimental results obtained on labelled faces in the wild, Yale B, AR, and FERET face databases demonstrate that the recognition rate in the proposed method is improved compared with other CFs.

Cancelable face verification using optical encryption and authentication.

In a cancelable biometric system, each instance of enrollment is distorted by a transform function, and the output should not be retransformed to the original data. This paper presents a new cancelable face verification system in the encrypted domain. Encrypted facial images are generated by a double random phase encoding (DRPE) algorithm using two keys (RPM1 and RPM2). To make the system noninvertible, a photon counting (PC) method is utilized, which requires a photon distribution mask for information reduction. Verification of sparse images that are not recognizable by direct visual inspection is performed by unconstrained minimum average correlation energy filter. In the proposed method, encryption keys (RPM1, RPM2, and PDM) are used in the sender side, and the receiver needs only encrypted images and correlation filters. In this manner, the system preserves privacy if correlation filters are obtained by an adversary. Performance of PC-DRPE verification system is evaluated under illumination variation, pose changes, and facial expression. Experimental results show that utilizing encrypted images not only increases security concerns but also enhances verification performance. This improvement can be attributed to the fact that, in the proposed system, the face verification problem is converted to key verification tasks.

Fusion of License Plate and Face Recognition for Secure Parking

Integration of multimodal biometrics is widely explored for the purpose of security enhancement. Hence, in this research we deemed further to evaluate the integration of car plate and face recognition as security enhancement in parking lot. Firstly, the acquired face and car plate that acted as database is encrypted based on Hill Cipher matrix manipulation using random numbers and Fast Fourier Transform (FFT) as encryption algorithm. Next, Unconstrained Minimum Average Correlation Energy (UMACE) was applied for car plate recognition process during parking entrance in identifying the intruder via Peak to Side lobe Ratio (PSR) value. During exit, UMACE is once again utilized for both plate and face recognition for verification of registered driver based on decision fusion of PSR value. Results attained specifically Total Success Rate (TSR) of 96% during parking entrance along with over 99% during exit at PSR value of 10, confirmed that the proposed method is apt as security in parking space.

Face verification using local binary pattern–unconstrained minimum average correlation energy correlation filters

In this paper, we present a novel approach for face verification using local binary pattern (LBP) operators and optical correlation filters. Due to selected filter type and LBP method, different performances would have been expected. We let LBP operate on training images to form local binary pattern-unconstrained minimum average correlation energy (LBP-UMACE) filters as an optical correlation filter to enhance recognition rates and reduce error rates simultaneously. As a result, we demonstrate that the designed filters have better performance compared with UMACE filters. Moreover, the proposed filters are easy to implement and can be used in fast and reliable face verification systems.

Anomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy Filter

Problem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR) of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI) was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.