initial commit

.gitattributes

@@ -0,0 +1,60 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,62 @@
+---
+tags:
+- face detection
+- facial feature extraction
+- identity recognition
+- smart device face recognition
+- security surveillance
+- identity verification systems
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+pretty_name: Face Recognition Image Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Face Recognition Image Dataset
+
+Currently, face recognition is widely used in smart devices, but factors such as environmental changes and lighting effects pose challenges to recognition accuracy. Existing datasets often lack diversity and annotation quality, limiting the algorithm&#039;s performance improvement. This dataset aims to improve the accuracy of recognition algorithms by providing a rich diversity of high-quality face images. During data collection, various types of camera equipment were used to shoot under different lighting and environmental conditions to ensure data diversity. Multiple rounds of annotation and consistency checks were utilized to ensure annotation accuracy, with the annotation team comprising experienced professionals. Data preprocessing includes steps such as denoising, alignment, and normalization. Data is organized in JPG format for easy storage and access. The core advantage of this dataset is its high-quality data annotation, with annotation accuracy exceeding 98%, and the adoption of new data augmentation techniques such as random cropping and rotation, which improve model robustness. Compared to similar datasets, this dataset holds advantages in diversity and annotation consistency. The provided high-precision data significantly improves the performance of face recognition models in low-light environments, with a 15% increase in recognition rate. Additionally, the dataset covers various skin tones, ages, and genders, offering high rarity and broad generalizability suitable for various face recognition application scenarios.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| face_count | int | The number of faces that can be recognized in the image. |
+| emotion_expression | string | The primary emotional expression shown on the face, such as smiling or angry. |
+| age_estimation | int | The estimated age value based on the face. |
+| gender_prediction | string | Gender prediction result based on the face, such as male or female. |
+| face_orientation | string | Information about the face's orientation, such as frontal, left profile, or right profile. |
+| image_brightness | float | The average brightness value of the image. |
+| skin_tone | string | The primary skin tone type recognized from the face. |
+| accessories_count | int | The number of accessories visible on the recognized face, such as glasses or hats. |
+| occlusion_level | float | The estimated level of face occlusion, ranging from 0 to 1. |
+
+## Compliance Statement
+
+<table>
+  <tr>
+    <td>Authorization Type</td>
+    <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
+  </tr>
+  <tr>
+    <td>Commercial Use</td>
+    <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
+  </tr>
+  <tr>
+    <td>Privacy and Anonymization</td>
+    <td>No PII, no real company names, simulated scenarios follow industry standards</td>
+  </tr>
+  <tr>
+    <td>Compliance System</td>
+    <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
+  </tr>
+</table>
+
+## Source & Contact
+
+If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/e79ac0fbfb9dff35ee9662e21351f51f?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com