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24 results for “Facial Masks”
Sample of facial mask N95 and FFP2 pricing on retail webs and time evolution per country
<p>We've gathered - for a Data Science educational project - the pricing of several face mask for breathing protection in a given period of time.</p> <p>Countries : Spain', 'USA', 'France', 'UK', 'Germany', 'Italy', 'Netherlands', 'Australia'</p> <p> </p> <p>'asin' type: STRING "Código de identifícación único de product equivalente de AMAZON"</p> <p>'description' type: STRING 'Texto descriptivo del producto'</p> <p>'dateTime' type: TIMESTAMP 'Cadena de carateres que contiene fecha y hora GMT'</p> <p>'date' Type: DATETIME ' Formato diferente de la misma fecha / hora de captura '</p> <p>'country' type: STRING 'Pais al que pertenece a distribución del producto 'Valores posibles: '</p>
Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings
<p><strong>Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings</strong></p><p> </p><p>The Masked Emotion FilmClip Dataset (MEFD) stands as an avant-garde assembly of emotion-inducing video clips tailored for a unique niche - the elicitation of emotions in individuals wearing facial masks. This dataset emerges in response to the global need to understand emotional cues and expressions in the backdrop of widespread facial mask usage. Assembled by leveraging the synergies between cinematography and psychological research, MEFD serves as an invaluable trove for researchers, especially those in AI, seeking to decode emotions even when a significant portion of the face is concealed.</p><p><strong>Dataset Highlights</strong>:</p><ul><li><strong>Facial Masks</strong>: All subjects in the video clips are seen wearing facial masks, replicating real-world scenarios and augmenting the dataset's relevance.</li><li><strong>Film Titles</strong>: The title of each selected film enriching the context of the emotional narrative.</li><li><strong>Emotion Label</strong>: Clear emotion classification associated with every clip, ensuring replicability in emotional elicitation.</li><li><strong>Clip Duration</strong>: Precise duration details ensuring standardized exposure and consistent emotion elicitation.</li><li><strong>Curated with Expertise</strong>: Clips have undergone rigorous evaluation by seasoned psychologists and film critics, affirming their effectiveness in eliciting the designated emotion.</li><li><strong>Consent and Ethics</strong>: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li></ul><p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p><p>Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395</p><p>A paramount feature of MEFD is its emphasis on "key moments". These timestamps, a product of collective expertise from psychologists and film critics, guide the researcher to intervals of heightened emotional resonance within the clips, especially challenging to discern with masked faces.</p><p><strong>Key Emotional Moments within Dataset</strong>:</p><p>Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334</p><p> </p><p>-----------------</p><p>DATA STRUCTURE<br>-----------------</p><p>SADNESS_XXX.CSV<br>timestamp emotion<br>1625062890.938222 NEUTRAL --> Initial time start for the neutral video<br>1625062932.567609 SADNESS --> Initial time start for the EMOTION video</p><p><br>Notes:<br>** Subject id 15: FEAR label started to fast; Neutral data very few<br>-----------------</p><p> </p><p><i>The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.</i></p><p>MEFD is more than just a dataset; it is a testament to human resilience and adaptability. As facial masks become ubiquitous, understanding the nuances of masked emotional expressions becomes imperative. MEFD rises to this challenge, bridging gaps and pioneering a new frontier in emotion research.</p>
Face mask detection and masked facial recognition dataset (MDMFR Dataset)
<p>The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0170">MDMFR, 2022</a>) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, <a href="https://www.sciencedirect.com/topics/computer-science/illumination-condition">illumination conditions</a>, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0215">Plastiliq, 2022</a>).</p>
Evaluation of the Efficacy and Tolerance of a Light Therapy Mask on Mild to Moderate Brown Spots and Moderate to Severe Facial Wrinkles
ClinicalTrials.gov study NCT03312543. IPD Sharing: NO. Countries: 1. Publications: 5.
Effectiveness of Facial Mask NIV in Adults Under General Anesthesia: Two-Hand C-E vs V-E Techniques
ClinicalTrials.gov study NCT07179432. IPD Sharing: YES. Countries: 1. Publications: 4.
Prevention of Damage Induced by Facial Mask Ventilation
ClinicalTrials.gov study NCT01351155. IPD Sharing: Not stated. Countries: 1. Publications: 1.
poStoperative Anesthesia Care: Facial Mask vs Hfnc and Thoracic Ultrasound for Reduction of Atelectasis Incidence
ClinicalTrials.gov study NCT04566419. IPD Sharing: NO. Countries: 1. Publications: 1.
Body Massage Oil, Facial Mask, and Ready-to-drink Jelly From Snake Fruit
ClinicalTrials.gov study NCT06227260. IPD Sharing: NO. Countries: 1. Publications: 1.
Reduction in COVID-19 Infection Using Surgical Facial Masks Outside the Healthcare System
ClinicalTrials.gov study NCT04337541. IPD Sharing: NO. Countries: 1. Publications: 7.
Comparison of the Efficacy of High-flow Nasal Oxygen Therapy and Facial Mask Ventilation on the Increase of the Oxygen Reserve Index During Anesthetic Induction
ClinicalTrials.gov study NCT04291339. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effect of Wearing Facial Masks on Skin Parameters During the COVID-19 Pandemic
ClinicalTrials.gov study NCT05277324. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Under the Nose Face Mask to Prevent Facial Pressure Ulcers During NIV for Acute Hypercapnic Respiratory Failure (AHRF)
ClinicalTrials.gov study NCT04102735. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Quality of Ventilation With Facial Versus Nasal Mask vs Nasal Mask Anesthesia in Children 3 to 12 Years Old
ClinicalTrials.gov study NCT05018468. IPD Sharing: YES. Countries: 1. Publications: 0.
HFNC Compared With Facial Mask in Patients With Chest Trauma Patients
ClinicalTrials.gov study NCT05828030. IPD Sharing: NO. Countries: 1. Publications: 0.
Efficacy of Mechanical Ventilation With Facial Mask to Reduce Gastric Insufflation
ClinicalTrials.gov study NCT01652924. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Facial Serum and Mask and Ready-to-drink Jelly From Thai Rice
ClinicalTrials.gov study NCT06475222. IPD Sharing: NO. Countries: 1. Publications: 0.
Benefits of Using a Transparent Visor to Replace the Face Mask in Speech Therapy Rehabilitation of Oral-Linguo-Facial Praxies in the Context of COVID-19: a Series of Cases
ClinicalTrials.gov study NCT04639427. IPD Sharing: NO. Countries: 1. Publications: 0.
Facial Mask Tightness: A Comparative Study
ClinicalTrials.gov study NCT02778984. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Evaluation and Study of the Efficacy of a Centella Asiatica-Infused Facial Mask on Discosmetic Dermatosis
ClinicalTrials.gov study NCT06763367. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
3D Printed Mask Adapter Designed From Facial 3D Scans for Fit Testing.
ClinicalTrials.gov study NCT05143814. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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OpenNeuro
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