Improving Face Sketch Recognition via Adversarial Sketch-Photo Transformation

International audiencefeature learning [7]-[10]. The benefit of the former category relates to the conversion of sketches into the same modality as photos, and hence lies in the ability to utilize existing photo-based face recognition methods. Thus, the applicability of the existing photo-based face recognition algorithms can be greatly expanded. Current methods for face photo-sketch transformation can be mainly grouped into example-based methods and regression-based methods. Example-based methods assume that the corresponding sketches (or patches of sketches) of two similar face photos (or patches of face photos) are also similar. Such methods rely on face photo-sketch pairs in the training set to synthesize images. In order to achieve good transformation results, these methods usually require a large number of photo-sketch pairs. However, the computational cost may also grow linearly with the increase of the training set size. Regression-based methods overcome the issues mentioned above and the most time-consuming part only exists in the training stage when learning the mapping between face photos and sketches, but the inference/testing stage can be fast. In this paper, we propose a Generative Adversarial Network (GAN) for face sketch-to-photo transformation , leveraging the advantages of CycleGAN [11] and conditional GANs [12]. We have designed a new feature-level loss, which is jointly used with the traditional image-level adversarial loss to ensure the quality of the synthesized photos. The proposed approach outperforms state-of-the-art approaches for synthesizing photos in terms of structural similarity index (SSIM). More importantly, the synthesized photos of our approach are found to be more instrumental in improving the sketch-to-photo matching accuracy. The rest of this paper is organized as follows: Section II summarizes representative methods of face photo-to-sketch transformation, and GANs. Section III provides details of the proposed method and the designed feature-level loss. Experimental results and analysis are presented in Section IV. Finally, we conclude this work in Section V. Abstract-Face sketch-photo transformation has broad applications in forensics, law enforcement, and digital entertainment, particular for face recognition systems that are designed for photo-to-photo matching. While there are a number of methods for face photo-to-sketch transformation, studies on sketch-to-photo transformation remain limited. In this paper, we propose a novel conditional CycleGAN for face sketch-to-photo transformation. Specifically, we leverage the advantages of CycleGAN and conditional GANs and design a feature-level loss to assure the high quality of the generated face photos from sketches. The generated face photos are used, as a replacement of face sketches, and particularly for face identification against a gallery set of mugshot photos. Experimental results on the public-domain database CUFSF show that the proposed approach is able to generate realistic photos from sketches, and the generated photos are instrumental in improving the sketch identification accuracy against a large gallery set

The effect of water temperature on the pathogenicity of decapod iridescent virus 1 (DIV1) in Litopenaeus vannamei

Decapod iridescent virus 1 (DIV1) has caused huge losses to the shrimp breeding industry in recent years as a new shrimp virus. In this study, white leg shrimp, Litopenaeus vannamei, were cultured at different temperatures (26 ± 1 °C and 32 ± 1 °C) and the same salinity, then infected with DIV1 by intramuscular injection to determine the effects of water temperature on viral infection. The DIV1 copy counts in the gills, hepatopancreas, pleopods, intestines, and muscles of L. vannamei were measured in samples collected at 6, 12, and 24 h post-infection (hpi), and the survival rate of L. vannamei was assessed every 6 h after infection. At 96 hpi, the survival rates of L. vannamei in the high (32 ± 1 ℃) and standard (26 ± 1 ℃) water temperature groups were 2.22% and 4.44%, respectively. The peak time of mortality in the high-water temperature group was 6 h earlier than in the standard water temperature group. After 24 hours of DIV1 infection, the DIV1 copy counts in the standard water temperature treatment group were significantly higher than those in the high-water temperature treatment group. The tissues with the highest virus copy counts in the standard and high-temperature groups were the intestines (2.9×1011 copies/g) and muscles (7.0×108 copies/g). The effect of temperature on the pathogenicity of DIV1 differs from that of other previously studied viruses, such as white spot syndrome virus, Taura syndrome virus, and infectious hypodermal and hematopoietic necrosis virus, because the high-water temperature did not mitigate the damage caused by DIV1 infection

Molecular Characterization of Organosulfates in Organic Aerosols from Shanghai and Los Angeles Urban Areas by Nanospray-Desorption Electrospray Ionization High-Resolution Mass Spectrometry

Fine aerosol particles in the urban areas of Shanghai and Los Angeles were collected on days that were characterized by their stagnant air and high organic aerosol concentrations. They were analyzed by nanospray-desorption electrospray ionization mass spectrometry with high mass resolution (m/Δm = 100,000). Solvent mixtures of acetonitrile and water and acetonitrile and toluene were used to extract and ionize polar and nonpolar compounds, respectively. A diverse mixture of oxygenated hydrocarbons, organosulfates, organonitrates, and organics with reduced nitrogen were detected in the Los Angeles sample. A majority of the organics in the Shanghai sample were detected as organosulfates. The dominant organosulfates that were detected at two locations have distinctly different molecular characteristics. Specifically, the organosulfates in the Los Angeles sample were dominated by biogenic products, while the organosulfates of a yet unknown origin found in the Shanghai sample had distinctive characteristics of long aliphatic carbon chains and low degrees of oxidation and unsaturation. The use of the acetonitrile and toluene solvent facilitated the observation of this type of organosulfates, which suggests that they could have been missed in previous studies that relied on sample extraction using common polar solvents. The high molecular weight and low degree of unsaturation and oxidization of the uncommon organosulfates suggest that they may act as surfactants and plausibly affect the surface tension and hygroscopicity of atmospheric particles. We propose that direct esterification of carbonyl or hydroxyl compounds by sulfates or sulfuric acid in the liquid phase could be the formation pathway of these special organosulfates. Long-chain alkanes from vehicle emissions might be their precursors

EFFECTS OF TEMPERATURE ON THE BENDING PERFORMANCE OF WOOD-BASED PANELS

The effect of temperature in the range from 25 ºC to 175 ºC on the bending performance of plywood and medium density fiberboard (MDF) has been studied with the ultimate purpose of optimizing the post-processing using radio frequency heating and improving the quality of the final products. Static 3-point bending tests were conducted on a universal testing machine inside a computer-controlled chamber. Results show that the bending strength (MOR) and modulus of elasticity (MOE) of plywood and MDF decrease with the increase of the temperature from 25 ºC to 175 ºC. The bending strength of plywood and MDF decreases with the increase of the exposure time. However, the effects of exposure time on MOE of plywood and MDF are not obvious. Plywood and 2.6 mm thick MDF show a typical elasto-plastic behavior, while 12 mm thick MDF does not exhibit any plastic behavior. It is recommended that the post-processing procedure should be completed within 15 minutes for both MDF and plywood

Improving Face Sketch Recognition via Adversarial Sketch-Photo Transformation

International audiencefeature learning [7]-[10]. The benefit of the former category relates to the conversion of sketches into the same modality as photos, and hence lies in the ability to utilize existing photo-based face recognition methods. Thus, the applicability of the existing photo-based face recognition algorithms can be greatly expanded. Current methods for face photo-sketch transformation can be mainly grouped into example-based methods and regression-based methods. Example-based methods assume that the corresponding sketches (or patches of sketches) of two similar face photos (or patches of face photos) are also similar. Such methods rely on face photo-sketch pairs in the training set to synthesize images. In order to achieve good transformation results, these methods usually require a large number of photo-sketch pairs. However, the computational cost may also grow linearly with the increase of the training set size. Regression-based methods overcome the issues mentioned above and the most time-consuming part only exists in the training stage when learning the mapping between face photos and sketches, but the inference/testing stage can be fast. In this paper, we propose a Generative Adversarial Network (GAN) for face sketch-to-photo transformation , leveraging the advantages of CycleGAN [11] and conditional GANs [12]. We have designed a new feature-level loss, which is jointly used with the traditional image-level adversarial loss to ensure the quality of the synthesized photos. The proposed approach outperforms state-of-the-art approaches for synthesizing photos in terms of structural similarity index (SSIM). More importantly, the synthesized photos of our approach are found to be more instrumental in improving the sketch-to-photo matching accuracy. The rest of this paper is organized as follows: Section II summarizes representative methods of face photo-to-sketch transformation, and GANs. Section III provides details of the proposed method and the designed feature-level loss. Experimental results and analysis are presented in Section IV. Finally, we conclude this work in Section V. Abstract-Face sketch-photo transformation has broad applications in forensics, law enforcement, and digital entertainment, particular for face recognition systems that are designed for photo-to-photo matching. While there are a number of methods for face photo-to-sketch transformation, studies on sketch-to-photo transformation remain limited. In this paper, we propose a novel conditional CycleGAN for face sketch-to-photo transformation. Specifically, we leverage the advantages of CycleGAN and conditional GANs and design a feature-level loss to assure the high quality of the generated face photos from sketches. The generated face photos are used, as a replacement of face sketches, and particularly for face identification against a gallery set of mugshot photos. Experimental results on the public-domain database CUFSF show that the proposed approach is able to generate realistic photos from sketches, and the generated photos are instrumental in improving the sketch identification accuracy against a large gallery set

Positioning of Apple’s Growth Cycle Based on Pattern Recognition

The positioning of the apple growth cycle plays a very important role in predicting the development of apples and guiding fruit farmers in agricultural operations. The traditional method of manually positioning the apple growth cycle has problems such as low efficiency and poor accuracy. Pattern recognition provides support for continuous and rapid positioning during the apple growth process. Under the natural conditions of the orchard, due to the large differences in the individual colors of the apples during the growth process and the influence of factors such as light changes, the photographed apple images are more complex, which brings certain difficulties to the segmentation and recognition of the apples. In this paper, pattern recognition is used to automatically identify and extract the growth stages of apples, a hue intensity (HI) color segmentation algorithm based on a Gaussian distribution model based on prior knowledge is studied, and then an active shape model (ASM) is used to identify each period of apple growth based on pattern recognition. After a series of experimental verifications, the ASM-based automatic identification method proposed in this paper is feasible and can identify the various growth periods of apples, thereby serving the mechanized production of apples

Tomato Maturity Recognition Model Based on Improved YOLOv5 in Greenhouse

Due to the dense distribution of tomato fruit with similar morphologies and colors, it is difficult to recognize the maturity stages when the tomato fruit is harvested. In this study, a tomato maturity recognition model, YOLOv5s-tomato, is proposed based on improved YOLOv5 to recognize the four types of different tomato maturity stages: mature green, breaker, pink, and red. Tomato maturity datasets were established using tomato fruit images collected at different maturing stages in the greenhouse. The small-target detection performance of the model was improved by Mosaic data enhancement. Focus and Cross Stage Partial Network (CSPNet) were adopted to improve the speed of network training and reasoning. The Efficient IoU (EIoU) loss was used to replace the Complete IoU (CIoU) loss to optimize the regression process of the prediction box. Finally, the improved algorithm was compared with the original YOLOv5 algorithm on the tomato maturity dataset. The experiment results show that the YOLOv5s-tomato reaches a precision of 95.58% and the mean Average Precision (mAP) is 97.42%; they are improved by 0.11% and 0.66%, respectively, compared with the original YOLOv5s model. The per-image detection speed is 9.2 ms, and the size is 23.9 MB. The proposed YOLOv5s-tomato can effectively solve the problem of low recognition accuracy for occluded and small-target tomatoes, and it also can meet the accuracy and speed requirements of tomato maturity recognition in greenhouses, making it suitable for deployment on mobile agricultural devices to provide technical support for the precise operation of tomato-picking machines