Part Of Face Research Articles

Survey Paper for Real Time Car Driver Drowsiness Detection using Machine Learning Approach

Abstract: Tiredness is a way wherein one level of mindfulness is decreased due to lacking of rest or weariness and it might cause the driver fall into rest unobtrusively. A Drowsy or lethargic driver can't decide while he/she can have a crazy rest. Nod off pulverizes are exceptionally outrageous as far as injury.Recent insights gauge that every year 1,200 passings and 76,000 wounds can be caused to fatigue or sluggishness related crashes. Over 25% of interstate car crashes are caused as consequence of driver weariness. Decrease the gamble of a mishap by advance notice the driver of his/her tiredness. This undertaking is fundamentally founded on four parts 1) Face and Eye identification: Performs scale invariant location utilizing Haar Cascade Classifier perform through a webcam. 2) Eye highlight extraction: Eye highlights are removed utilizing Hough Circle and 3) Extract single eye 4) Edge location and perform tiredness discovery on it. In the proposed technique, following the face identification step, the facial parts those are more significant and considered as the best for tiredness, are removed and followed in video succession outlines. The framework has been tried and carried out in a genuine climate. The commitment work is when tiredness recognized, after it will give alert admonition sign to the driver.

CQA-Face: Contrastive Quality-Aware Attentions for Face Recognition

Few existing face recognition (FR) models take local representations into account. Although some works achieved this by extracting features on cropped parts around face landmarks, landmark detection may be inaccurate or even fail in some extreme cases. Recently, without relying on landmarks, attention-based networks can focus on useful parts automatically. However, there are two issues: 1) It is noticed that these approaches focus on few facial parts, while missing other potentially discriminative regions. This can cause performance drops when emphasized facial parts are invisible under heavy occlusions (e.g. face masks) or large pose variations; 2) Different facial parts may appear at various quality caused by occlusion, blur, or illumination changes. In this paper, we propose contrastive quality-aware attentions, called CQA-Face, to address these two issues. First, a Contrastive Attention Learning (CAL) module is proposed, pushing models to explore comprehensive facial parts. Consequently, more useful parts can help identification if some facial parts are invisible. Second, a Quality-Aware Network (QAN) is developed to emphasize important regions and suppress noisy parts in a global scope. Thus, our CQA-Face model is developed by integrating the CAL with QAN, which extracts diverse quality-aware local representations. It outperforms the state-of-the-art methods on several benchmarks, demonstrating its effectiveness and usefulness.

Facial expression recognition under constrained conditions using stacked generalized convolution neural network

A cognitive-analysis of facial features can make facial expression recognition system more robust and efficient for Human-Machine Interaction (HMI) applications. Through this work, we propose a new methodology to improve accuracy of facial expression recognition system even with the constraints like partial hidden faces or occlusions for real time applications. As a first step, seven independent facial segments: Full-Face, half-face (left/right), upper half face, lower half face, eyes, mouth and nose are considered to recognize facial expression. Unlike the work reported in literature, where arbitrarily generated patch type occlusions on facial regions are used, in this work a detailed analysis of each facial feature is explored. Using the results thus obtained, these seven sub models are combined using a Stacked Generalized ensemble method with deep neural network as meta-learner to improve accuracy of facial expression recognition system even in occluded state. The accuracy of the proposed model improved up to 30% compared to individual model accuracies for cross-corpus seven model datasets. The proposed system uses CNN with RPA compliance and is also configured on Raspberry Pi, which can be used for HRI and Industry 4.0 applications which involve face occlusion and partially hidden face challenges.

Learning inter-class optical flow difference using generative adversarial networks for facial expression recognition

Facial expression recognition is a fine-grained task because different emotions have subtle facial movements. This paper proposes to learn inter-class optical flow difference using generative adversarial networks (GANs) for facial expression recognition. Initially, the proposed method employs a GAN to produce inter-class optical flow images from the difference between the static fully expressive samples and neutral expression samples. Such inter-class optical flow difference is used to highlight the displacement of facial parts between the neutral facial images and fully expressive facial images, which can avoid the disadvantage that the optical flow change between adjacent frames of the same video expression image is not obvious. Then, the proposed method designs four-channel convolutional neural networks (CNNs) to learn high-level optical flow features from the produced inter-class optical flow images, and high-level static appearance features from the fully expressive facial images, respectively. Finally, a decision-level fusion strategy is adopted to implement facial expression classification. The proposed method is validated on two public facial expression databases, BAUM_1a, SAMM and AFEW5.0, demonstrating its promising performance.

Eyes versus Eyebrows: A Comprehensive Evaluation Using the Multiscale Analysis and Curvature-Based Combination Methods in Partial Face Recognition

This work aimed to find the most discriminative facial regions between the eyes and eyebrows for periocular biometric features in a partial face recognition system. We propose multiscale analysis methods combined with curvature-based methods. The goal of this combination was to capture the details of these features at finer scales and offer them in-depth characteristics using curvature. The eye and eyebrow images cropped from four face 2D image datasets were evaluated. The recognition performance was calculated using the nearest neighbor and support vector machine classifiers. Our proposed method successfully produced richer details in finer scales, yielding high recognition performance. The highest accuracy results were 76.04% and 98.61% for the limited dataset and 96.88% and 93.22% for the larger dataset for the eye and eyebrow images, respectively. Moreover, we compared the results between our proposed methods and other works, and we achieved similar high accuracy results using only eye and eyebrow images.

Evaluation of the relationship between nasal septal deviation and development of facial asymmetry with anthropometric measurements depending on age

AimIt was aimed to determine the change of facial asymmetry resulting from nasal septal deviation (SD) depending on age, gender, degree of deviation and the affected area besides the effect of SD on somatotype and craniofacial morphology. Materials and methods171 volunteers (90 males, 81 females), 27 individuals aged 9–13, 44 individuals aged 14–18, 44 individuals aged 19–23 and 56 individuals in control group participated in the study conducted in otorhinolaryngology polyclinic.11 photometric, 16 anthropometric measurements were taken from the participants. ResultsSD affects facial asymmetry formation, although not statistically significant compared to healthy individuals asymmetry rates (p˃0.05). It was determined that the degree of SD affected asymmetry only between the ages of 14–18 (in adolescence) and the development of asymmetry in all SD patients was not statistically dependent on age and gender (p˃0.05). Photometric measurements demonstrated asymmetries in horizontally-extending parameters of 1/3 middle part of face. There was no statistically significant difference in the cranial anthropometric measurements of the upper and lower 1/3 of the face compared to the control group (p˃0.05). The order of the most asymmetrical parameters is Alare-Zygion, Alare-Subnasale, Cheilion-Gonion, Exocanthion-Cheilion, Midsagittal plane-Zygion, Zygion-Cheilion, Zygion-Gonion, Subalare-Cheilion, Glabella-Exocanthion. In all participants were determined that endomorph somatotype was dominant in female and mesomorph somatotype was dominant in male besides SD did not affect somatotype and somatotype did not alter with age. ConclusionThe development of facial asymmetry due to SD is not affected by age and gender furthermore SD does not affect craniofacial asymmetry and somatotype.

Septoplasty and septorhinoplasty in children

Surgical correction of the nasal septum in childhood remains a controversial issue. Due to concerns that destruction of growth zones in the trapezius cartilage during the procedure may have a significant impact on the later growth of the nose and facial part of cranium, for many years ENT specialists approached the issue of septoplasty in children in a very conservative manner. However, many more recent reports indicate that a conservatively and correctly performed procedure does not pose a threat to the development of the nose and can be safely performed in children with severe septal deviation after 5–6 years of age. In case of the coexistence of deformities in the shape of the external nose, it is recommended to simultaneously correct it, which guarantees an optimal final result. These age restrictions do not apply to urgent cases, such as inter alia: septal hematoma and abscess, tumors and developmental disorders. Most importantly, no or sparing (with preservation of cartilage growth centers) resection of the quadrangular cartilage is essential to avoid nasal growth disorder. Rhinoplasties have been reported to have a higher rate of repair and lower patient aesthetic satisfaction. The decision if to perform surgical treatment and its scope should be made after analyzing the potential benefits and consequences on the one hand and leaving a deviated septum on the other hand. In case of doubt, the operation should be postponed until the end of puberty.

Twenty years of investigation with the case of prosopagnosia PS to understand human face identity recognition. Part I: Function

Following traumatic brain injury in adulthood, Pierrette Sapey (PS) became suddenly unable to recognize the identity of people from their faces. Thanks to her remarkable recovery of general brain function, liveliness, and willingness to be tested, PS's case of prosopagnosia has been extensively studied for more than 20 years. This investigation includes hundreds of hours of behavioral data collection that provide information about the nature of human face identity recognition (FIR). Here a theory-driven extensive review of behavioral and eye movement recording studies performed with PS is presented (part I). The specificity of PS's recognition disorder to the category of faces, i.e., with preserved visual object (identity) recognition, is emphasized, arguing that isolating this impairment is necessary to define prosopagnosia, offering a unique window to understand the nature of human FIR. Studies performed with both unfamiliar and experimentally or naturally familiar faces show that PS, while being able to perceive both detailed diagnostic facial parts and a coarse global facial shape, can no longer build a relatively fine-grained holistic visual representation of a face, preventing its efficient individuation. Her mandatory part-by-part analytic behavior during FIR causes increased difficulties at extracting diagnostic cues from the crowded eye region of the face, but also from relative distances between facial parts and from 3D shape more than from surface cues. PS's impairment is interpreted here for the first time in terms of defective (access to) cortical memories of faces following brain damage, causing her impaired holistic perception of face individuality. Implications for revising standard neurofunctional models of human face recognition and evaluation of this function in neurotypical individuals are derived.

A New Conulariid (Cnidaria, Scyphozoa) From the Terminal Ediacaran of Brazil

Paraconularia ediacaran. sp., the oldest documented conulariid cnidarian, is described based on a compressed thin specimen from the terminal Ediacaran Tamengo Formation near Corumbá, Mato Grosso do Sul State, Brazil. The conulariid was collected from a laminated silty shale bed also containingCorumbella werneriand vendotaenid algae. The specimen consists of four partial faces, two of which are mostly covered, and one exposed corner sulcus. The two exposed faces exhibit 32 bell-curve-shaped, nodose transverse ribs, with some nodes preserving a short, adaperturally directed interspace ridge (spine). The transverse ribs bend adapertureward on the shoulders of the corner sulcus, within which the ribs terminate, with the end portions of the ribs from one face alternating with and slightly overlapping those from the adjoining face. This is the first Ediacaran body fossil showing compelling evidence of homology with a particular conulariid genus. However, unlike the periderm of Phanerozoic conulariids, the periderm ofP.ediacaralacks calcium phosphate, a difference which may be original or an artifact of diagenesis or weathering. The discovery ofP.ediacarain the Tamengo Formation corroborates the hypothesis, based in part on molecular clock studies, that cnidarians originated during mid-late Proterozoic times, and serves as a new internal calibration point, dating the split between scyphozoan and cubozoan cnidarians at no later than 542 Ma. Furthermore,P. ediacarareinforces the argument that the final phase of Ediacaran biotic evolution featured the advent of large-bodied eumetazoans, including, possibly, predators.

Facial expression recognition in facial occlusion scenarios: A path selection multi-network

The occlusion scenarios including wearing mask, wearing sunglasses, wearing hat, etc. Thus, parts of face are occluded, e.g. nose and mouth are obscured if a mask is worn. Facial expression recognition (FER) tasks have been widely researched. However, less attention has been paid to FER in occlusion scenarios, which are not uncommon in the real world. In this paper, we propose a method that structures a path selection multi-network model to achieve the FER in the above three types of facial occlusion scenarios. The method contains two parts. For the multi-network, we segment the labels in one database which results in three new sub-databases to train three Subnets, respectively. For the path selection, which is an integration method of multi-network, we merge groups of labels in one database to train an initial network called BeginNet. The prediction of BeginNet selects one of the Subnets to make the final prediction. We concatenate four popular databases Fer2013, JAFFE, KDEF and Raf-DB databases into one larger database, and use the combined database to verify our method effectiveness. The experimental results show that our method has better results in coping with the expression recognition task in multiple types of facial occlusion scenarios.

An Exploratory Data Analysis for Synchronous Online Learning Based on AFEA Digital Images

The spread of COVID-19 throughout the world has affected the education sector. In some higher education institution, such as Polytechnic Caltex Riau (PCR), it is mandatory for students to participate in synchronous or asynchronous learning activities via virtual classroom. Synchronous online learning is usually supported by video conferencing media such as Google Meeting or Zoom Meeting. The communication between lecturers and students is captured as an image as evidence of students’ interaction and participation in certain learning subjects. These images can provide information for lecturers in determining students’ internal feelings and measuring students’ interest through facial emotions. Taking this reason into account, the current research aims to analyze the emotions detected in facial expression through images using automatic facial expression analysis (AFEA) and exploratory data analysis (EDA), then visualize the data to determine the possible solution to improve the educational process’ sustainability. The AFEA steps applied were face acquisition to detect facial parts in an image, facial data extraction and representation to process feature extraction on the face, and facial expression recognition to classify faces into emotional expressions. Thus, this paper presents the results obtained from applying machine learning algorithms to classify facial expressions into happy and unhappy emotions with mean values of 5.58 and 2.70, respectively. The data were taken from the second semester of 2020/2021 academic year with 1,206 images. The result highlighted the fact that students showed the facial emotion based on the lecture types, hours, departments, and classes. It indicates that there are, in fact, several factors contributing to the variances of students’ facial emotions classified in synchronous online learning.

Face image synthesis from facial parts

Recently, inspired by the growing power of deep convolutional neural networks (CNNs) and generative adversarial networks (GANs), facial image editing has received increasing attention and has produced a series of wide-ranging applications. In this paper, we propose a new and effective approach to a challenging task: synthesizing face images based on key facial parts. The proposed approach is a novel deep generative network that can automatically align facial parts with the precise positions in a face image and then output an entire facial image conditioned on the well-aligned parts. Specifically, three loss functions are introduced in this approach, which are the key to making the synthesized realistic facial image: a reconstruction loss to generate image content in an unknown region, a perceptual loss to enhance the network's ability to model high-level semantic structures and an adversarial loss to ensure that the synthesized images are visually realistic. In this approach, the three components cooperate well to form an effective framework for parts-based high-quality facial image synthesis. Finally, extensive experiments demonstrate the superior performance of this method to existing solutions.

Fully convolutional Deep Stacked Denoising Sparse Auto encoder network for partial face reconstruction

Face recognition is one of the most successful applications of image analysis. Since 1960s, automatic face recognition research has been carried out, but the problem is still unresolved. Therefore, in this manuscript, a novel Partial face reconstruction (PFR) algorithm called Self- motivated feature mapping (SMFM) combining a Fully Convolutional Network (FCN) and Deep Stacked Denoising Sparse Autoencoders (DS-DSA) algorithm is proposed to overcome the challenges. The proposed approach focuses on the generation of feature maps from the Fully Convolutional Network and it is used Deep Stacked Denoising Sparse Autoencoders to perform the partial face reconstruction. The spatial maps are generated by extracting the features from Fully Convolutional Network and it is supplied as the input for partial reconstruction and re-identification to the Deep Stacked Denoising Sparse Autoencoders network. The main aim of the proposed work is “to enhance the accuracy during facial reconstruction”. The proposed approach is implemented in MATLAB platform. The performance of the proposed approach attains 23.45% and 20.41% accuracy,25.93`% and 19.43% sensitivity, 22.21% and 24.41% precision and20.21% and 23.41% Specificity greater than the existing approaches, like Partial Face Reconstruction using generative adversarial networks (GANs), Partial Face Reconstruction using Deep Recurrent neural network (DRNN).

Aerodynamics of a tall building equipped with a solid screen close to the façade

Given the importance recently assumed by permeable building envelopes in modern building architecture, this work addresses the aerodynamic effects of a screen placed close to the front face of a prismatic tall building with a square base. The screen is non-porous and covers the entire building face, but it is open on the lateral sides so to create a thin cavity in connection with the external wind flow. Pressure measurements are carried out in the wind tunnel reproducing a scaled suburban turbulent wind profile. A wind perpendicular to the screened face is assumed as baseline case study, but different azimuthal angles of the wind direction are also considered. Two distances of the screen are tested, namely 1/20 and 1/40 of the building base side length. The experiments reveal a significant aerodynamic influence of the screen, similar to the one observed in previous two-dimensional studies, though less pronounced. For a wind perpendicular to the screen, the major effects of the screen on the building aerodynamics are the decrease of the mean pressure in the upstream part of the side faces, a faster recover of it moving towards the back corners, and a marked increase of the slope of the lateral force coefficient, even implying positive quasi-steady aerodynamic damping in case of cross-flow vibrations. For inclined wind directions, the flow in the cavity has a significant influence on the pressure distribution over the building, generally reducing the pressure coefficients in the region where the flow is ejected.

How Sleep-Deprived People See and Evaluate Others' Faces: An Experimental Study.

BackgroundAcute sleep loss increases the brain’s reactivity toward positive and negative affective stimuli. Thus, despite well-known reduced attention due to acute sleep loss, we hypothesized that humans would gaze longer on happy, angry, and fearful faces than neutral faces when sleep-deprived. We also examined if facial expressions are differently perceived after acute sleep loss.MethodsIn the present, within-subjects study, 45 young adults participated in one night of total sleep deprivation and one night with an 8-hour sleep opportunity. On the morning after each night, an eye tracker was used to measure participants’ time spent fixating images of happy, angry, fearful, and neutral faces. Participants also evaluated faces’ attractiveness, trustworthiness, and healthiness on a 100-mm visual analog scale.ResultsFollowing sleep loss, participants struggled more fixating the faces than after sleep. The decrease in total fixation duration ranged from 6.3% to 10.6% after sleep loss (P<0.001). Contrary to our hypothesis, the reduction in total fixation duration occurred irrespective of the displayed emotion (P=0.235 for sleep*emotion interaction) and was also present for the upper (P<0.001) but not the lower part of the faces (except for the lower part of angry faces). Overall, faces were evaluated as less trustworthy (−2.6 mm) and attractive (−3.6 mm) after sleep loss (p<0.05).DiscussionFacial expressions are crucial for social interactions. Thus, spending less time fixating on faces after acute sleep loss may come along with several problems for social interactions, eg, inaccurate and delayed judgment of the emotional state of others. In addition, more negative social impressions of others may lead to social withdrawal in sleep-deprived humans.

Exploring the Distribution and Orientation of Cell Proliferation as Drivers of Mouse Facial Development

During the development of the face, tissues move, change shape, and fuse in tightly orchestrated patterns to create all the parts of a normal face. These shape changes are driven by factors such as cell signaling, migration, proliferation, and apoptosis. However, the contributions of each of these drivers to morphogenesis are poorly studied. Here, we explore differential cell proliferation as a driver of mouse facial morphogenesis. We quantify patterns in both the spatial distribution and orientation of proliferation in the developing face in 3D over a critical period of murine facial development (E9.5‐E11.5). We use immunostaining with light sheet microscopy (LSM) to capture total and proliferating nuclei. To compare proliferative density in facial tissues, we segment these images using a novel convolutional neural network. We then generate atlases of average proliferation at each half‐day age point within our range and use these to identify relationships between morphology and cell proliferation. We show that regions with more dense proliferation tend to undergo more intensive shape changes. We then simulate outgrowth of the maxillary process using a cell simulation engine, PhysiCell, to demonstrate that differential proliferation is necessary to maintain expected morphology in growing tissues. In addition to differential proliferation, localized orientation of cell division could also affect morphology. In plants and some animal tissues, including murine limb buds, preferentially oriented cell proliferation drives shape change by causing tissue elongation in specific directions. We explore the orientation and distribution of cell proliferation using LSM: we inject pregnant dams with a synthetic nucleotide, EdU, 5 minutes before harvest to mark the daughter cells of proliferative events occurring in the interim. We then compare the angles of the proliferative axes for each pair of daughters relative to the primary direction of tissue growth. Preliminary results suggest that cell proliferation in the maxillary and nasal processes is oriented preferentially towards the axis of tissue growth. These results suggest that both the distribution and orientation of cell proliferation play a role in murine facial morphogenesis. Understanding the mechanisms underlying morphogenesis is important to guide future research that could lead to earlier and more robust diagnosis and treatment of syndromes and facial abnormalities.

Anatomical distribution of mandibular fractures from severe bicycling accidents: A 12-year experience from a Norwegian level 1 trauma center.

Background/AimThe mandible makes up a substantial part of the lower face, and is susceptible to injury. Even in helmeted cyclists, accidents may lead to fractures of the mandible because conventional helmets provide little protection to the lower part of the face. In addition, some studies indicate that helmets may lead to an increased risk of mandibular fractures. Thus, the aim of this study was to examine the anatomic distribution of mandibular fractures in injured cyclists and to assess if helmet use influenced the fracture locations.Material and MethodsData from a Norwegian Level 1 trauma center were collected in the Oslo University Hospital Trauma Registry over a 12‐year period. Of 1543 injured cyclists, the electronic patient charts of 62 cyclists with fractures of the mandible were retrospectively evaluated in detail. Demographic data, helmet use, and fracture type were assessed.ResultsSixty‐two patients (4%) had fractures of the mandible, and women had an increased risk (OR 2.49, 95% CI 1.49–4.16, p < .001). The most common fracture site was the mandibular body, followed by the condyle. Isolated mandibular fractures occurred in 45% of the patients and 55% had other concomitant facial fractures. There were 42% of the patients with fractures in multiple sites of the mandible, and 42% had a concomitant dentoalveolar injury. Half of the cyclists were wearing a helmet at the time of the accident and 39% were not. There was no significant difference in fracture distribution between the helmeted and non‐helmeted groups.ConclusionsFracture of the mandibular body was the most prevalent mandibular fracture type following bicycle accidents. Women had an increased risk of mandibular fractures compared with men, whereas helmet wearing did not affect the anatomical fracture site.

Face recognition under mask-wearing based on residual inception networks

PurposeThis paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.Design/methodology/approachThe proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.FindingsThe proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.Originality/valueThe convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

Automatic Facial Expression Analysis and Recognition using Zone Based Active and Salient Facial Patches

Recognition of facial expression has many useful applications that have drawn researcher’s interest over the past decade. Extraction of features is a major step in the analysis of expression which leads to fast and accurate recognition of expression. Recognition of facial expressions is not an easy issue for methods of machine learning, as different people can vary in the way they show their expressions and for one expression the image of the same person can differ for brightness, background and position. Recognition of facial expression is therefore still a challenging computer vision problem. In this thesis work, we aim to design a robust technique of automatic facial expression analysis and recognition using zone based active and salient patches of the human face by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition where a lot of research has been done and a vast array of algorithms have been introduced. Facial expression recognition (FER) can also be considered as a special case of a pattern recognition problem and many techniques are available. In the designing of an FER system, we divided the system into 4 modules, i.e. preprocessing, active and salient patch extraction and classification. Voila Jones algorithm is used for face detection and after that features are extracted from the facial patches. The active facial patches are located on the facial regions that during different expressions undergo a major change. The active patches are located after detection of facial landmarks and hybrid features are determined from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost. Zoning is applied and got remarkable results. The dimensionality of the function is reduced by using linear discriminant analysis, which is further defined using the support vector machine (SVM). On the basis of classification expression is recognized. We evaluated our algorithm on Extended Cohn-Kanade (CK+) dataset.

CLINICAL AND MORPHOLOGICAL SUBSTANTIATION OF INTRANASAL LYMPHOMAS OF CATS

The article discusses results of the research on intranasal lymphoma spread of cats, based on the study of 38 clinical cases of animals with lesions of the nasal cavity at the age of 5 months to 18 years with clinical symptoms from the total number (N=4937) of cats. The research was conducted at the Department of Diseases of Small Domestic, Laboratory and Exotic animals of FSBEI HE "Moscow State University of Food Production" and the veterinary clinic "Zoogalereya", Moscow. Intranasal neoplasms are rare for cats. Of all nasal oncopathies, lymphomas are most common in animals of the middle age group and, according to the literature data, take up to 1% of all tumors of cats. Endogenous and exogenous factors that cause tumors of cats’ nasal cavity are diverse. If intranasal pathology is suspected, it is necessary to adhere to the plan of diagnostic manipulations, because the first clinical symptoms of the process are the same for lesions of a different nature. For diagnosis and specification of the clinical stage of the disease, it is necessary to conduct a series of diagnostic studies, including radiography, MRI and CT, aimed at determining the localization of the pathology, spread, degree of invasion and degree of involvement of blood vessels in a specific oncological process. Such diagnostic methods as palpation of regional lymph nodes, chest radiographs and ultrasonography of the abdominal organs are aimed at determining the clinical stage of oncopathologies. The most predisposed to occurrence of intranasal lymphomas was a group of cats aged 5-9 years. Gender and breed susceptibility was not revealed. In case of nasal cavity pathologies of cats, a common symptom is shortness of breath, lack of patency of nasal passages, which appears at later stages of the disease. Nasal discharge and epistaxis are rare, convulsive seizures - when the tumor invades via the ethmoid bone. The predominance of unilateral over bilateral discharge was noted as the most common symptom, 39.4%, while the nature of the discharge may be different. Sneezing occurred in 13.2%, coughing in 5.2% of cases. Deformation of the facial part of the skull was observed in 28.9% of cats, epistaxis, lacrimation and atony - in 13.2% of cases, 5.2% of cats had neurological symptoms.