Gender Classification Using Face Vectors: A Deep Learning Approach Without Classical Models
Abstract
1. Introduction
1.1. Background
1.2. Related Work
1.3. Motivation
1.4. Contribution and Novelty
- ➢
- The research has demonstrated that gender recognition can be achieved without the utilization of any classification algorithm, employing solely 128B average face representation vectors for dlib, 512B for ArcFace, and 512B for FaceNet512.
- ➢
- During this study, a Python-based web scraping method was developed to prepare the datasets.
- ➢
- The face detection process incorporated the RetinaFace (RetinaFace-10GF) and CenterFace (centerface.onnx) models.
- ➢
- The face representation vectors were extracted using dlib’s “dlib_face_recognition_resnet_model_v1,” ArcFace’s (ResNet50@WebFace600K), and FaceNet512’s (facenet512_weights.h5) deep neural network models.
- ➢
- The data type known as “JSON” was utilized for the purpose of storing the face representation vectors.
- ➢
- The novelty of the approach presented in this paper is that the classical vector distance measure is used as a classifier without using a deep learning-based model in the classification process.
2. Materials and Method
2.1. Creating the Dataset
2.2. Face Detecting and Face Embedding Methods
2.3. Average Neural Face Embeddings (ANFE) Model
- The training dataset is segmented into two distinct groups, designated as “male” and “female”.
- The faces in each image in the two different groups in the training dataset are detected with the RetinaFace and CenterFace deep network models. (Consequently, a series of experiments were conducted with the objective of enhancing detection accuracy and the minimum size threshold for the face region was set to 40 pixels).
- The face representation vectors of the detected and aligned faces are extracted with face recognition-based deep network models, including dlib, ArcFace, and FaceNet512. From these models, the 128-D face representation vectors are extracted with dlib and the 512-D face representation vectors are extracted with ArcFace and FaceNet512.
- Within each gender group, the extracted face representation vectors are then summed and divided to create an average face representation vector for that group, which is saved as JSON data. The mathematical representation of this structure is given in Equation (1) [72]. In the equation, m represents the number of samples, i.e., the number of people, and X is the face embedding (feature vector) extracted for each person.
Algorithm 1: Generation of gender-specific mean face embeddings |
Input: Image dataset grouped by gender (female, male) Output: Averaged facial embeddings for each gender and model Initialize gender_embeds for each model in {ArcFace, Dlib, Facenet512} For each gender in {female, male}: Load image list from dataset Shuffle image list randomly count ← 0 For each image in image list: If image format is unsupported: continue For each model in {ArcFace, Dlib, Facenet512}: Detect face and extract embedding using DeepFace If one face is detected with confidence ≥ 0.8 and face_size ≥ 40: Add embedding to gender_embeds[model][gender] Update sum of embeddings count ← count + 1 If count ∈ {100, 200, 300, 400, 500, 1000}: For each model: Compute average embedding: avg ← sum/count Store avg in gender_embeds[model][gender][count] Convert all embeddings to list format Save gender_embeds to JSON file |
- The test dataset is segmented into two distinct groups, designated as “male” and “female.”
- The faces in each image in the two different groups in the test dataset are detected with the RetinaFace and CenterFace deep network models.
- Subsequently, the face representation vectors of the detected and aligned faces are extracted with the face recognition-based deep network models dlib, ArcFace, and FaceNet512. From these models, the 128-D face representation vectors are extracted with dlib and the 512-D face representation vectors are extracted with ArcFace and FaceNet512.
- For each face, L2-normalization is used to calculate the Euclidean distance of the extracted face representation vectors to the average male and female face representation vectors. The minimum value is designated as the predicted gender group. The mathematical representation of the Euclidean distance is given in Equation (2) [72].
Algorithm 2: Gender-specific mean face representation vector model test |
Initialize gender embedding results dictionary for each model: ArcFace, Dlib, Facenet512 Load average gender embeddings from previously computed data: For each model in [ArcFace, Dlib, Facenet512]: Load average embeddings for female and male at various sample sizes (e.g., 100, 200, 300, 400, 500, 1000) For each gender in [female, male]: Retrieve image paths from test dataset Randomly shuffle image list For each image in the list (up to 500 samples): For each face detector in preferred list: Detect face in the image using the current detector If one face is detected with confidence ≥ 0.8 and face_size ≥ 40: Extract face embedding using the specified recognition model Store image-level info (embedding, bounding box, detection score, etc.) For each sample size (100–1000): Calculate Euclidean-L2 distances to average female and male embeddings Predict gender based on closer distance Record prediction outcome and whether it matches ground truth Break face detection loop Save the aggregated gender prediction results to a JSON file |
3. Experimental Results
4. Discussion
- True Positive (TP): True positive predictions (those predicted by the model to be positive that are in fact positive).
- True Negative (TN): True negative predictions (those predicted by the model to be negative that are actually negative).
- False Positive (FP): False positive predictions (those that the model predicts as positive that are actually negative).
- False Negative (FN): False negative predictions (those that the model predicts as negative but are actually positive).
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Type | Age Range | Number of Data | Number of Subjects | Changed Parameters |
---|---|---|---|---|---|
FG-NET [4] | Controlled | Various age progressions | 1.000 | 82 | Lighting, background, resolution variations |
CLF [5] | Controlled | Children | 10.000 | 3000 (longitudinal images with age progression) | - |
UIUC-IFP-Y [6] | Controlled | - | 8.000 | 1.600 | Asian descent, age labels |
MORPH [7] | Controlled | - | 55.000 | 13.000 | Ethnicity, date of birth, gender |
VADANA [8] | Controlled | Multiple images of different ages | 2.298 | 43 | Age progression, multiple images |
CACD [9] | Controlled | - | 163.446 | 2000 | Birth dates |
FERET [10] | Controlled | - | - | - | Warm-up images with various features |
LFW [11] | Uncontrolled | - | - | - | Faces collected from the internet, age and gender estimation |
IMDB-WIKI [12] | Uncontrolled | from 10 to 90 | 500,000 | - | Various challenges |
LAP [13] | Uncontrolled | - | 4.699 | - | Age labels |
Adience [14] | Uncontrolled | - | - | - | Cross-sectional dataset, labels for age groups |
ANFE Model Id | Number of Data | Female | Male | ||||
---|---|---|---|---|---|---|---|
True | False | % | True | False | % | ||
100 | 500 | 376 | 124 | 75.20 | 359 | 141 | 71.80 |
200 | 500 | 389 | 111 | 77.80 | 352 | 148 | 70.40 |
300 | 500 | 385 | 115 | 77.00 | 357 | 143 | 71.40 |
400 | 500 | 380 | 120 | 76.00 | 364 | 136 | 72.80 |
500 | 500 | 380 | 120 | 76.00 | 358 | 142 | 71.60 |
1000 | 500 | 377 | 123 | 75.40 | 361 | 139 | 72.20 |
100 | 1000 | 766 | 234 | 76.60 | 740 | 260 | 74.00 |
200 | 1000 | 786 | 214 | 78.60 | 728 | 272 | 72.80 |
300 | 1000 | 779 | 221 | 77.90 | 737 | 263 | 73.70 |
400 | 1000 | 771 | 229 | 77.10 | 748 | 252 | 74.80 |
500 | 1000 | 770 | 230 | 77.00 | 738 | 262 | 73.80 |
1000 | 1000 | 764 | 236 | 76.40 | 743 | 257 | 74.30 |
ANFE Model Id | Number of Data | Female | Male | ||||
---|---|---|---|---|---|---|---|
True | False | % | True | False | % | ||
100 | 500 | 456 | 44 | 91.20 | 442 | 58 | 88.40 |
200 | 500 | 454 | 46 | 90.80 | 433 | 67 | 86.60 |
300 | 500 | 452 | 48 | 90.40 | 436 | 64 | 87.20 |
400 | 500 | 459 | 41 | 91.80 | 439 | 61 | 87.80 |
500 | 500 | 459 | 41 | 91.80 | 437 | 63 | 87.40 |
1000 | 500 | 460 | 40 | 92.00 | 442 | 58 | 88.40 |
100 | 1000 | 919 | 81 | 91.90 | 901 | 99 | 90.10 |
200 | 1000 | 928 | 72 | 92.80 | 901 | 99 | 90.10 |
300 | 1000 | 924 | 76 | 92.40 | 898 | 102 | 89.80 |
400 | 1000 | 930 | 70 | 93.00 | 896 | 104 | 89.60 |
500 | 1000 | 931 | 69 | 93.10 | 896 | 104 | 89.60 |
1000 | 1000 | 920 | 80 | 92.00 | 902 | 98 | 90.20 |
ANFE Model Id | Number of Data | Female | Male | ||||
---|---|---|---|---|---|---|---|
True | False | % | True | False | % | ||
100 | 500 | 485 | 15 | 97 | 469 | 31 | 93.8 |
200 | 500 | 488 | 12 | 97.6 | 472 | 28 | 94.4 |
300 | 500 | 486 | 14 | 97.2 | 470 | 30 | 94 |
400 | 500 | 486 | 14 | 97.2 | 471 | 29 | 94.2 |
500 | 500 | 486 | 14 | 97.2 | 471 | 29 | 94.2 |
1000 | 500 | 486 | 14 | 97.2 | 471 | 29 | 94.2 |
100 | 1000 | 968 | 32 | 96.80 | 937 | 63 | 93.70 |
200 | 1000 | 975 | 25 | 97.50 | 942 | 58 | 94.20 |
300 | 1000 | 971 | 29 | 97.10 | 941 | 59 | 94.10 |
400 | 1000 | 972 | 28 | 97.20 | 941 | 59 | 94.10 |
500 | 1000 | 972 | 28 | 97.20 | 941 | 59 | 94.10 |
1000 | 1000 | 972 | 28 | 97.20 | 941 | 59 | 94.10 |
Model Name | Female | Male | Precision | Recall | F1 Score | Accuracy | ||||
---|---|---|---|---|---|---|---|---|---|---|
True | False | % | True | False | % | |||||
VGG-Face (DeepFace [73]) | 887 | 113 | 88.7 | 968 | 32 | 96.8 | 0.97 | 0.89 | 0.92 | 0.93 |
Small-VGG-Face16 [74] | 964 | 36 | 96.4 | 733 | 267 | 73.3 | 0.78 | 0.96 | 0.86 | 0.85 |
Gender-Caffemodel [75] | 662 | 378 | 66.2 | 888 | 112 | 88.8 | 0.85 | 0.62 | 0.72 | 0.76 |
imdb-tensorflow [76] | 880 | 120 | 88.0 | 800 | 200 | 80.0 | 0.81 | 0.88 | 0.85 | 0.84 |
ArcFace 200-ANFE (our) | 928 | 72 | 92.80 | 901 | 99 | 90.10 | 0.94 | 0.97 | 0.96 | 0.96 |
FaceNet512 200-ANFE (our) | 975 | 25 | 97.50 | 942 | 58 | 94.20 | 0.94 | 0.97 | 0.96 | 0.96 |
Model Name | VGG-Face | Small-VGG-Face16 | Gender-Caffemodel | imdb-Tensorflow | dlib | FaceNet512 | ArcFace |
---|---|---|---|---|---|---|---|
Embedding Size | 2622D | 128D | - | - | 128D | 512D | 512D |
Place of Use | Face recognition and comparison | Face recognition (light version) | Gender estimation | Age and gender estimation | Extracts the face embedding vector | Extracts the face embedding vector | Extracts the face embedding vector |
Introduction Visual Size | 224 × 224 | 224 × 224 | 227 × 227 | 64 × 64 | 150 × 150 | 160 × 160 | 112 × 112 |
Model Type | VGG-16-based (deeper structure) | Reduced VGG (fewer parameters) | CaffeNet-like structure | CNN–LSTM combination (Keras/Tensorflow) | ResNet-34-like CNN | Inception-ResNet-v1 | ResNet-100/MobileFaceNet/SwinTransformer |
Loss Function | - | - | - | - | Pair-wise hinge loss (margin-based) | Triplet loss | ArcFace loss (additive angular margin loss) |
Dataset | VGGFace dataset (~2.6 M images, 2.6 K people) | Retrained with constrained data | Adience dataset | IMDB-WIKI dataset (~500 K images) | Special small set without face | VGGFace2 (3.3 M images, 9.1K people) | MS1MV2 (5.8 M images, 85 K people)|Glint360k (17 M images, 360 K people) |
Dataset Size | 2.6 million images, 2622 people | ~1 million images (approx.) | Adience: 26,000+ images, IMDB-WIKI: 500,000+ images | ~525,000 tagged face images (originally 1 million+) | Estimated ~10 K images | ~3.3 million images | MS1MV2: ~5.8 M|Glint360k: ~17 M |
Accuracy (LFW) | 95–97% (on well-aligned data) | 92–94% (suitable for lightweight systems) | ~90% gender prediction accuracy | Age ±4 years, gender accuracy 88–90 | ~97% | ~99.2% | ~99.8% |
Size Model | ~500 MB | ~90 MB | ~85 MB | ~100 MB | Light (~80 MB) | Medium (150–200 MB) | Large (250 MB+) |
Real Time | Poor on CPU, acceptable with GPU | Suitable for CPU/GPU | Yes | Limited—GPU preferred | Yes | Possible with GPU | Difficult on large models; light with MobileFaceNet |
Library Support | DeepFace, Keras | DeepFace, TensorFlow | Caffe, OpenCV | TensorFlow, DeepFace | dlib, face_recognition | deepface, facenet-pytorch, keras-facenet | insightface, onnxruntime, torch, mxnet |
Advantage | High accuracy thanks to depth | Lightweight, suitable for embedded systems | Fast and simple prediction model | Age + gender prediction in one | Fast, simple, small applications | Balanced accuracy and speed | Highest accuracy, industrial-quality results |
Model Name | Our Dataset | Average Processing Time (s) (Just for One Face) |
---|---|---|
VGG-Face (deepface) [73] | AsianWiki | 260 |
Small-VGG-Face16 [74] | AsianWiki | 59 |
Gender-Caffemodel [75] | AsianWiki | 13 |
imdb-tensorflow [76] | AsianWiki | 65 |
ArcFace 200-ANFE (our) | AsianWiki | 1 |
FaceNet512 200-ANFE (our) | AsianWiki | 1 |
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Makinist, S.; Aydin, G. Gender Classification Using Face Vectors: A Deep Learning Approach Without Classical Models. Information 2025, 16, 531. https://doi.org/10.3390/info16070531
Makinist S, Aydin G. Gender Classification Using Face Vectors: A Deep Learning Approach Without Classical Models. Information. 2025; 16(7):531. https://doi.org/10.3390/info16070531
Chicago/Turabian StyleMakinist, Semiha, and Galip Aydin. 2025. "Gender Classification Using Face Vectors: A Deep Learning Approach Without Classical Models" Information 16, no. 7: 531. https://doi.org/10.3390/info16070531
APA StyleMakinist, S., & Aydin, G. (2025). Gender Classification Using Face Vectors: A Deep Learning Approach Without Classical Models. Information, 16(7), 531. https://doi.org/10.3390/info16070531