Biometrics is one of the most widely used approaches for the identification of an individual using physiological or behavioural characteristics such as the face, iris, finger vein or gait [1]. Biometric systems are advantageous because they do not require a person to carry cards or remember information, unlike conventional authentication systems based on smart cards or passwords. Possession-based authentication systems have the disadvantage that keys and tokens can be shared, misplaced, duplicated, lost or stolen, whereas biometric systems avoid these problems [2]. Thus, biometrics has been adopted in many applications. However, unimodal biometric systems (based on a single modality such as the face or fingerprint) face several problems, such as illumination variation, skin condition and the environment, and device variations [3]. For example, the performance of face recognition is easily degraded by the facial pose, expression and various illumination conditions. Iris recognition performance is greatly affected by the huge area of near infrared (NIR) light reflection that hides the iris area, dense eyelashes and defocusing of the input image. To overcome the limitations of unimodal biometrics, much attention has been paid to multimodal biometrics [4]. Multimodal biometrics aims to identify the individual based on two or more human physiological or behavioural characteristics. The key problem of multimodal biometrics is the method used to combine multiple features from each modality to produce better recognition results. Many studies and algorithms have been proposed for multimodal biometric fusion [5, 6]. The fusion of multimodal biometric system information can be performed at three different levels, i.e., feature level, matching score level and decision level. A popular method is to fuse at the matching score level because it easily facilitates the combination of scores from different matching systems such as face and fingerprint recognition systems [5]. Thus, we focus on fusion at the matching score level to integrate matching scores for the face and both irises in this study. There have been many previous studies of multimodal biometric systems that combine the face with a palmprint or the face with the iris, etc. [4]. Previous multimodal biometric systems based on face and iris recognition have used face and single iris features because of the high accuracy of iris recognition and the convenience of face recognition [7–9]. However, there has been little research on the combination of the face and both irises because of the increased system complexity. The irises of a single person are known to be as different as those of different people [10], so we propose a new multimodal biometric method for combining information from the face and both irises, thereby guaranteeing higher accuracy. In addition, in their experiments, the face and iris data were acquired by combining two different open databases of face and iris, even with the face and iris of the same person [7–9]. This is on the basis of an assumption that the face and iris are perfectly uncorrelated. However, intensive statistical analyses are required for confirming this assumption and we performed the experiments with data on the face and both irises which were actually acquired from persons instead of two different open databases of face and iris. To simultaneously capture images of the face and both irises, our proposed device consists of a face camera, two iris cameras, NIR illuminators and cold mirrors. Rapid and accurate iris detection based on two circular edge detections (CED), which are accomplished in the iris image on the basis of the size of the iris detected in the face image. The accuracy is enhanced by combining the three matching scores for the face and both irises, with recognition based on a support vector machine (SVM). The remainder of the paper is organized as follows. Section 2 presents the proposed system and methods. Section 3 and section 4 provide the experimental results and conclusions, respectively.