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This paper proposes a low-cost personal identification system that uses the combined palm vein and palmprint biometric features. The system consists of near-infrared and visible light-emitting diode (LED) arrays, a low-cost visual sensor, a Xilinx chip, and other components. A real-time image quality assessment (IQA) method for the combined palm vein and palmprint biometric features is also proposed. Two types of the LED with central frequency spectra of 890 and 680 nm are used to capture the palm vein and palmprint, respectively. The adaptive feedback control of the diode brightness is in accordance with the image quality assessed by the combined 2D entropy and local 2D entropy. The palm vein and palmprint images are acquired nearly simultaneously, and each acquired image undergoes a few preprocessing steps for extraction of the vein and print patterns. We use an image-level wavelet-based fusion strategy to reduce image storage requirement for the embedded platform and implement a complex wavelet-based fusion strategy for the PC platform. A deep scattering convolutional network is applied for extracting the features of the fused images, and a multi-class support vector machine is used for training and recognition. Characteristics of some vision-based personal identification systems are discussed. The proposed real-time IQA method with fusion strategy and feature extraction algorithm in our prototype system has substantially less operational requirements than that of the previous fusion strategies. It also demands less memory and yields lower equal error rate than the classical feature extraction algorithms.