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Comparative analysis of NAE values of image samples generated from GCT sketches versus original sketches
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Usage of sketches for offender recognition has turned out to be one of the law enforcement agencies and defense systems’ typical practices. Usual practices involve producing a convict’s sketch through the crime observer’s explanations. Nevertheless, researches have effectively proved the failure of customary practices as they carry a maximum level...
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The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries...
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... Feature-based approaches employ extracted characteristics from the eyes, nose, and mouth to detect a person's likeness in a drawing. The local binary pattern (LBP) technique is a popular feature-based approach since it can extract textural information from the drawing and utilize it for face recognition [4]. The core pixel's intensity levels are compared to those of it is neighbors, and the resulting binary values are used in the LBP feature extraction process. ...
This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.
... We have chosen non-referential quality assessors such as NIQE, BRISQUE and PIQE as our parameters for assessing the preprocessor's influence. Table demonstrates the effect of our preprocessor Sannidhan et al., 2021). ...
The identification of bacterial colonies is deemed to be crucial in microbiology as it helps in identifying specific categories of bacteria. The careful examination of colony morphology plays a crucial role in microbiology laboratories for the identification of microorganisms. Quantifying bacterial colonies on culture plates is a necessary task in Clinical Microbiology Laboratories, but it can be time‐consuming and susceptible to inaccuracies. Therefore, there is a need to develop an automated system that is both dependable and cost‐effective. Advancements in Deep Learning have played a crucial role in improving processes by providing maximum accuracy with a negligible amount of error. This research proposes an automated technique to extract the bacterial colonies using SegNet, a semantic segmentation network. The segmented colonies are then counted with the assistance of blob counter to accomplish the activity of colony counting. Furthermore, to ameliorate the proficiency of the segmentation network, the network weights are optimized using a swarm optimizer. The proposed methodology is both cost‐effective and time‐efficient, while also providing better accuracy and precise colony counts, ensuring the elimination of human errors involved in traditional colony counting techniques. The investigative assessments were carried out on three distinct sets of data: Microorganism, DIBaS, and tailored datasets. The results obtained from these assessments revealed that the suggested framework attained an accuracy rate of 88.32%, surpassing other conventional methodologies with the utilization of an optimizer.
... To evaluate the operational capability of our proposed skin tone analyzer module, we have incorporated three standard evaluation procedures [31] explained below: ...
The process of identifying a criminal is extremely challenging if no eyewitnesses are available or closedcircuit television (CCTV) footage does not show the criminal's face. With the use of high-definition cameras and soft biometrics, forensic science has made great strides in identifying criminals based on their bodily traits. Due to the use of one type of soft biometric for identification, many researchers have focused on this area and have failed to achieve better matching. Despite the fact that similar soft biometrics share many of the same features, one type of soft biometric matching has been insufficient and resulted in inaccurate results. As a result, it becomes difficult to make distinct classifications. Therefore, this study proposes the implementation of a suspect identification system that uses mole pattern analysis and skin tone matching as soft biometrics. The deep residual pooling network is employed to analyze mole patterns, while the histogram equalization method is employed to analyze skin tones. We have also improved the performance of the deep seg-net and the histogram equalization technique by incorporating an aggregator operator and an adaptive pixel-wise noise cancellation filter. The experimental results show that the proposed method attains higher accuracy when the loss rate is gradually decreased. The proposed method attains 89.21% accuracy which is higher than the existing approaches and the efficiency of the proposed method is compared with various texture encoders © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.
... A higher quality of training images also leads to the generation of better-quality images. Article [30] proposed a technique to generate facial photos from the sketch input using the conditional GAN (cGAN) model. In their work, to enhance the performance of their model, authors have incorporated a gamma correctionbased preprocessor to generate enhanced quality sketches from color photo images. ...
In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided by an observer. Additionally, we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated sketches. To evaluate the effectiveness of our proposed method, we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance metrics. Our proposed method achieved an overall inception score of 1.438 ± 0.049 and a Frechet Inception Distance of 65.29, outperforming other state-of-the-art techniques.
... Advent of transfer learning models in deep learning system increased the accuracy of image classification techniques with minimal number of datasets involving better ranked quality of images. Good number of research work also exposed the support of image preprocessors enhancing the training process of transfer learning models [20][21][22][23]. Article [24] conducted a thorough study on usage of pretrained transfer learning models for classifying wide range of image classes. ...
Traditional Car dents severity prediction involves skillful experts in fields that tends to be tedious and time-consuming. Considering the copious amounts of accidents, a robust, automated, cost-effective approach for detecting severity in car dents deems necessary. Our main goal is to utilize modern procedures for processing accident severity to overcome the problems associated with human visual error-prone problems. Mostly, vehicle accidents have specific fixed set of parameters that play a crucial role in determining the severity of the accident. In our system, we incorporate an approach named transfer learning for the image feature extraction process via pre-trained models for predicting severity of the dent in the vehicle. Our system initially extracts car image samples which are pre-processed for background elimination. These images are later passed on to our pre-trained neural network models for fine-tuning and feature extraction. Finally, we generate, and test various categories of dents based on their severity with existing pre-classified samples to enable high similarity score prediction. Several experimental analysis and exhaustive testing, our proposed system was able to detect vehicle dent severity with an overall accuracy of 96.34%, which is ably better than other existing computer-based classification methods.
... An experiment is conducted on every image and their result is presented in this section. Following are the description of quality metrics [20][21][22][23]. ...
Colour blindness is one of the minor disorders in individuals appearing due to a genetic mutation causing the intensity to few colours making the individual fail to discriminate specific colours. Concerning the nobility of colour recognition, the disorder ranges in differentiating monochromic colour to the most common form. This ‘red-green’ dissimilarity causes vibrant colours to override dull colours easily are easily seen, and the dull shades are difficult to perceive. Targeting the impacts of colour blindness, in our research work, we have proposed a designed colour filter built on the combination of the well-known Ishihara test t and histogram equalization. Ishihara filter alone is insufficient as the approach is not suited well to translate real-world images. To overcome this problem, the proposed work aimed at the modification Ishihara filter eradicating lighter shades and shifting them to the darker regions. The image obtained after applying histogram equalization could also be perceived by a colour-blind person. The research paper has also presented a comparative analysis of the two approaches for the elimination of colour blindness.
... The proposed organization recognizes the image then supplies skeletal information of all the body joints in three-dimensional spaces. [20][21][22][23] The extremit ies of the human hands form the joints of our interest. Our work primarily uses data of an image and the data corresponding to the depth for detecting and evaluating the gestures based on the end-user. ...
Hand gestures aid the user in unspoken communication and help interpret sign language. Modern nonverbal communication detection employs gesture recognition systems and allows us to recognize shapes and signals made by hand movements using numerous machine learning techniques. Gestures have a very high value in human-computer interaction and sign language detection systems. To implement the gesture recognition and detection the existing methodologies rely on the application of bodily sensors. Due to this, the effectiveness in communication becomes challenging and cumbersome. Hence targeting on the major fallacies of the existing approaches, the primary aspiration of this research work is to recognize hand gestures in real-time using depth sensors that support natural communication avoiding on body sensor dependency. A novel approach is presented to detect hand gestures using minimalistic training and the efficiency of other algorithms concerning this proposed technique has been compared. Experimental investigations on the proposed methodology have successfully confirmed that, the research exertion has obtained an overall gesture detection accuracy of 90.86% and a high precision of 98.25%.
Addressing the intricacies of facial aging in forensic facial recognition, traditional sketch portraits often fall short in precision. This study introduces a pioneering system that seamlessly integrates a de-aging module and a sketch generator module to overcome the limitations inherent in existing methodologies. The de-aging module utilizes a deepfake-based neural network to rejuvenate facial features, while the sketch generator module leverages a pix2pix-based Generative Adversarial Network (GAN) for the generation of lifelike sketches. Comprehensive evaluations on the CUHK and AR datasets underscore the system’s superior efficiency. Significantly, comprehensive testing reveals marked enhancements in realism during the training process, demonstrated by notable reductions in Frechet Inception Distance (FID) scores (41.7 for CUHK, 60.2 for AR), augmented Structural Similarity Index (SSIM) values (0.789 for CUHK, 0.692 for AR), and improved Peak Signal-to-Noise Ratio (PSNR) metrics (20.26 for CUHK, 19.42 for AR). These findings underscore substantial advancements in the accuracy and reliability of facial recognition applications. Importantly, the system, proficient in handling diverse facial characteristics across gender, race, and culture, produces both composite and hand-drawn sketches, surpassing the capabilities of current state-of-the-art methods. This research emphasizes the transformative potential arising from the integration of de-aging networks with sketch generation, particularly for age-invariant forensic applications, and highlights the ongoing necessity for innovative developments in de-aging technology with broader societal and technological implications.
