Age and gender predictions of unfiltered faces classify unconstrained real-world facial images into predefined age and gender. Significant improvements have been made in this research area due to its usefulness in intelligent real-world applications. However, the traditional methods on the unfiltered benchmarks show their incompetency to handle large degrees of variations in those unconstrained images. More recently, Convolutional Neural Networks (CNNs) based methods have been extensively used for the classification task due to their excellent performance in facial analysis. In this work, we propose a novel end-to-end CNN approach, to achieve robust age group and gender classification of unfiltered real-world faces. The two-level CNN architecture includes feature extraction and classification itself. The feature extraction extracts feature corresponding to age and gender, while the classification classifies the face images to the correct age group and gender. Particularly, we address the large variations in the unfiltered real-world faces with a robust image preprocessing algorithm that prepares and processes those faces before being fed into the CNN model. Technically, our network is pretrained on an IMDb-WIKI with noisy labels and then fine-tuned on MORPH-II and finally on the training set of the OIU-Adience (original) dataset. The experimental results, when analyzed for classification accuracy on the same OIU-Adience benchmark, show that our model obtains the state-of-the-art performance in both age group and gender classification. It improves over the best-reported results by 16.6% (exact accuracy) and 3.2% (one-off accuracy) for age group classification and also there is an improvement of 3.0% (exact accuracy) for gender classification.

Real and apparent age estimation of human face has attracted increased attention due to its numerous real-world applications. Different intelligent application scenarios can benefit from these computer-based systems that predict the ages of people correctly. Automatic apparent age system is particularly useful in medical diagnosis, facial beauty product development, movie role casting, the effect of plastic surgery, and anti-aging treatment. Predicting the real and apparent age of people has been quite difficult for both machines and humans. More recently, Deep learning with Convolutional Neural Networks (CNNs) methods have been extensively used for these classification task. It has incomparable advantages in extracting discriminative image features from human faces. However, many of the existing CNN-based methods are designed to be deeper and larger with more complex layers that makes it challenging to deploy on mobile devices with resource-constrained features. Therefore, we design a lightweight CNN model of fewer layers to estimate the real and apparent age of individuals from unconstrained real-time face images that can be deployed on mobile devices. The experimental results, when analyzed for classification accuracy on FG-NET, MORPH-II and APPA-REAL, with large-scale face images containing both real and apparent age annotations, show that our model obtains a state-of-the-art performance in both real and apparent age classification when compared to state-of-the-art methods. The new results and model size, therefore, confirm the usefulness of the model on resource-constrained mobile devices.

Intrusion detection in IoT networks is challenged by data heterogeneity, label scarcity, and privacy constraints. Traditional federated learning (FL) methods often assume IID data or require supervised labels, limiting their practicality. We propose G-PFL-ID, a graph-driven personalized federated learning framework for unsupervised intrusion detection in non-IID IoT systems. Our method trains a global graph encoder (GCN or GAE) with a DeepSVDD objective under a federated regularizer (FedReg) that combines proximal and variance penalties, then personalizes local models via a lightweight fine-tuning head. We evaluate G-PFL-ID on the IoT-23 (Mirai-based captures) and N-BaIoT (device-level dataset) under realistic heterogeneity (Dirichlet-based partitioning with concentration parameters $\alpha \in \{0.1,0.5,\infty \}$ and client counts $K\in \{10,15,20\}$ for IoT-23, and natural device-based partitioning for N-BaIoT). G-PFL-ID outperforms global FL baselines and recent graph-based federated anomaly detectors, achieving up to 99.46% AUROC on IoT-23 and 97.74% AUROC on N-BaIoT. Ablation studies confirm that the proximal and variance penalties reduce inter-round drift and representation collapse, and that lightweight personalization recovers local sensitivity—especially for clients with limited data. Our work bridges graph-based anomaly detection with personalized FL for scalable, privacy-preserving IoT security.