Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.
Background:
Face annotation is the naming procedure to assign the correct name of a
person who has emerged on an image.
Objective:
The main objective of this paper was to compare and evaluate six feature extraction techniques
for face annotation under real-time challenging images and to find the best suitable feature
for face annotation.
Method:
From literature review, it has been observed that Name Semantic Network (NSN) outperforms
other annotation methods for various unconditioned images as well as ambiguous tags.
However, the NSN’s performance can differ with various feature extraction techniques. Hence, its
success is influenced by the feature extraction techniques that are used. Therefore, in this work, the
NSN’s performance is experimented and evaluated with various feature extraction methods such as
the Discrete Cosine Transform Local Binary Pattern (DCT-LBP), Discrete Fourier Transform Local
Binary Pattern (DFT-LBP), Local Patterns of Gradients (LPOG), Gist, Local Order-constrained
Gradient Orientations (LOGO) and Convolutional Neural Networks (CNNs) deep features.
Results:
Different feature extraction approaches demonstrate variations in performance with respect
to a range of difficulties in face annotation using the Yahoo, LFW and IMFDB databases. The experimental
results show that the deep feature method can achieve better recognition rate other than
texture features. It confronts several issues in the presentation of a face in an image and produces
better results.
Conclusion:
It is concluded that the CNNs deep feature is the best feature extraction technique that
offers enhanced performance for face annotation.
Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.
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